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Automatic chord recognition (ACR) has long been a topic of interest in the field of Music Information Retrieval (MIR), due to not only its commercial applications, but also its support for advanced music analysis. While a lot of ACR-related work deals with audio data, ACR from symbolic music has received less attention. In addition, conventional ACR systems specify chords in a key-dependent way (usually with the root note and the chord quality) and hence are unable to reveal the high-level patterns and harmonic structures. These issues hinder the developments of music analysis and music generation via ACR systems. With the success of deep learning, it is viable to build a symbolic ACR system using a more comprehensive chord vocabulary such as functional harmony. Recently, two advanced models, namely the Bi-directional Transformer for Chord Recognition (BTC) and the Harmony Transformer (HT), introduced for the first time the multi-head attention mechanism to ACR, showing the great capability of the attention mechanism to improve the performance of ACR. In this paper, we systematically study the performance of the BTC and the HT in terms of symbolic ACR, and propose an improved model. Experiments on conventional ACR and advanced functional harmony recognition indicate that the HT has the potential to surpass the BTC, especially in terms of chord segmentation quality. Also the overall performance of the HT is further improved by enhancing the learning of local context and positional information.
Chord recognition is a process to identify the harmonic entity of each musical segment, usually by giving a chord name to the segment in question. This problem is not as simple as it may seem, for it concerns several aspects of musical harmony, and the answer to the problem may not be unique. For instance, a C major sixth chord, C6, in popular music may be termed as an A minor seventh chord in first inversion, Am7/C, in classical music. In other words, the chord vocabulary differs according to musical style and context. Moreover, the boundary of each segment which deserves to be recognized as a single chord is not explicitly defined by the music itself. Therefore, it is usually difficult to partition music into harmonically meaningful segments. A comprehensive chord recognition approach should specify the information of when to identify a chord and how to build the chord. Owing to its complexity, ACR is still challenging even nowadays.
During the past decades, researchers have studied ACR, particularly for audio data, from different aspects, such as chord segmentation (Yoshioka et al., 2004; Harte et al., 2006; Degani et al., 2015), beat and key (Zenz and Rauber, 2007), bass line (Yang et al., 2016), root notes (Yang et al., 2016), and meter (Degani et al., 2017). Although there are various approaches to ACR, much of the work targets calculation of effective chroma features for comparison with a set of chord templates (Fujishima, 1999; Lee, 2006; Stark and Plumbley, 2009; Oudre et al., 2011). Based on the chroma features, stochastic methods such as Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) are frequently employed to predict chord progressions (Sheh and Ellis, 2003; Cho and Bello, 2009; Ueda et al., 2010; Deng and Kwok, 2016; Korzeniowski and Widmer, 2016). This combination of audio feature extraction and chord sequence prediction can be analogous to the integration of acoustic modeling and language modeling in the field of speech recognition (Li and Wu, 2015), and has become a typical framework for ACR from audio.
Along with the rise of deep learning, ACR has experienced a paradigm shift from template-based algorithms to data-driven approaches. Works on ACR have begun to explore the capability of deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for extracting non-handcrafted features and for modeling chord sequences (Humphrey and Bello, 2012; Boulanger-Lewandowski et al., 2013; McFee and Bello, 2017; Hori et al., 2017). Besides, various chord vocabularies ranging from basic triads (Zhou and Lerch, 2015; Korzeniowski and Widmer, 2016) to more complex chords (Humphrey and Bello, 2015; Deng and Kwok, 2017; Jiang et al., 2019) have been taken into consideration in ACR systems. However, most of the audio datasets commonly used in ACR work provide only chord labels without the audio data because of copyright restrictions, preventing evaluation on the same benchmarks. Readers are recommended to refer to Pauwels et al. (2019) for a thorough review of ACR from audio during the past two decades.
In comparison to ACR from audio data, ACR from symbolic music has received less attention (Scholz and Ramalho, 2008; Rhodes et al., 2009; Rocher et al., 2009; Masada and Bunescu, 2019). One reason is that the public are more likely to access audio content rather than symbolic music. Another reason is that high-quality symbolic music data and the corresponding annotations are relatively scarce. The difference between audio ACR and symbolic ACR mainly lies in the processing of the music data. Audio music data contain expressive information (e.g., timbre) and are often represented as spectrograms or chromagrams using the Short-Time Fourier Transform (STFT) or the constant-Q transform (CQT). On the other hand, symbolic music data comprise abstract concepts of music (e.g., the pitch of each single note), and hence can be represented as sets of note-related features (Masada and Bunescu, 2017) or piano rolls (Chen and Su, 2018). In spite of such a difference, symbolic ACR and audio ACR are capable of achieving competitive performances with nearly identical neural network architectures, since the two types of data can be represented and structured in a similar way (Chen and Su, 2019). It has to be mentioned that symbolic ACR using deep learning approaches is still in its preliminary stage, and previous research employed different datasets (usually comprising limited amounts of data) for evaluation. Therefore, more systematic studies are needed to explore the capability of deep neural networks and to set benchmarks for this field.
The development of symbolic ACR is valuable from four points of view. First, while the majority of research by musicologists and music theorists focuses on symbolic music representations, relatively few symbolic ACR and related MIR tools are available. Second, symbolic music data are invariant to some aspects of musical interpretation and thus can reduce the bias resulted from the performing and the recording factors. Third, the analysis of symbolic music data can be easily related to the abstract aspect of music by which musicological insights are derived. Finally, the growing interest in music generation will yield a lot of symbolic music data (Dong and Yang, 2018; Donahue et al., 2019; Lim et al., 2020); hence an increase in demand for symbolic ACR would likely occur before long.
Functional harmony is an advanced way of describing chords, typically by performing Roman numeral (RN) analysis. Traditionally, RN analysis includes the specification of key (or tonic), modulation, chord quality, chord inversion, and chord alteration. Instead of merely giving a chord name to each harmonic entity, as is usually done in ACR work, RN analysis represents chords as RNs which indicate the root notes of the chords in relation to the tonic, and specify the harmonic functions (hence the term functional harmony). As a result, the RN representation is key-invariant, and provides more informative analysis of musical harmony. According to the aforementioned facts, the recognition of functional harmony involves more than identifying chords, and is more demanding than the conventional ACR.
While some research has contributed to functional harmony recognition (Tsui and MacLean, 2002; Raphael and Stoddard, 2004; Illescas et al., 2007; Passos et al., 2009; de Haas et al., 2011), very few of them employed deep learning methods, partly due to the limited amounts of training data. Fortunately, several symbolic corpora containing RN annotations have been published in the past few years, e.g., the TAVERN dataset (Devaney et al., 2015), the ABC dataset (Neuwirth et al., 2018), the BPS-FH dataset (Chen and Su, 2018), and the Bach Preludes (Tymoczko et al., 2019). In addition, researchers have begun to address the functional harmony recognition task using advanced deep learning techniques (Chen and Su, 2019; Micchi et al., 2020). Considering that functional harmony is a fundamental way to find common patterns in chord progressions and to uncover the tonal structure of a musical piece, research on harmony recognition will benefit both the symbolic MIR and the musicology communities (Gotham and Ireland, 2019).
As in language, chord progressions and musical harmony are highly context-dependent. Therefore, it is important to consider the relations between harmonic entities when recognizing chords and their functions. Although RNNs are representative models for capturing contextual information, the attention mechanism has shown its potential for sequence modeling (Bahdanau et al., 2015; Hermann et al., 2015; Parikh et al., 2016). The first fully attention-based model, namely the Transformer (Vaswani et al., 2017), was accordingly proposed to compete with the previously existing models of sequence learning. Its variant, BERT (Bi-directional Encoder Representations from Transformers) (Devlin et al., 2019), was devised to function as a pre-trained model for learning word representations. These attention-based models made a lot of breakthroughs in language-related tasks, and stood as landmarks in natural language processing (NLP). Due to its promising performance, the attention mechanism has been applied to many other tasks beyond the realm of NLP, such as image and music generation (Parmar et al., 2018; Huang et al., 2019).
Recently, two Transformer-based models, the bi-directional Transformer for chord recognition (BTC) (Park et al., 2019) and the Harmony Transformer (HT) (Chen and Su, 2019), were proposed for the first time to tackle the ACR task. The BTC utilized a self-attention mechanism to capture the long-term dependency in musical sequences, and showed its ability to segment chord sequences; the HT estimated chord transitions (or chord boundaries), and then recognized chords via attending to the segmentation-informed sequence.1 Although the two models were built upon the Transformer, they differed from each other in two aspects. First, the BTC utilized the encoder part of the Transformer, while the HT employed the entire Transformer architecture. Second, the BTC was trained on audio datasets, while the HT was applied to both audio and symbolic music data. In spite of these differences, the two models have demonstrated the effectiveness of the attention mechanism on modeling chord progressions, and outperformed other promising models in previous research (Korzeniowski and Widmer, 2016, 2017; McFee and Bello, 2017). Since the attention mechanism can access all positions of a sequence at a time, the Transformer-based models are theoretically more capable of capturing thorough knowledge of the sequence than RNNs, and therefore may alleviate the issue of temporal fragmentation when modeling chord sequences at the time-frame level (Korzeniowski and Widmer, 2017).
In this work, we tackle symbolic ACR using Transformer-based models, and propose improvements on the models. With evaluations on the conventional chord recognition and the functional harmony recognition tasks, we show that the HT is more promising than the BTC in terms of recognition accuracy and segmentation quality; it is also validated that the proposed improvements advance the overall performance of ACR. The major contribution of this paper is to propose an improved Transformer-based network for symbolic ACR, specifically on the challenging functional harmony recognition task, via a systematic investigation on the symbolic ACR research and Transformer-based ACR methods. The remainder of the paper is arranged as follows: we first introduce the Transformer, examine the architectures of the HT and the BTC, and propose methods to advance the performance of chord recognition (Section 2). Then we conduct experiments to evaluate the models and the proposed methods using two symbolic datasets (Section 3). The future work is then discussed (Section 4). Finally, the concluding remarks are presented (Section 5).
The Transformer comprises two major computational blocks: the multi-head attention (MHA) and the feed-forward network (FFN). For the MHA, the concept of key-value memory is adopted, in which the keys are used to address relevant memories with respect to a query, and the corresponding values are subsequently returned (Miller et al., 2016). For example, given a query to search for a document in a database, the search engine will map the query against a set of keys (e.g., title, description, etc.) associated with candidate documents in the database, then present the best matched documents (values). In the case of sequence-to-sequence learning, the queries stand for the target sequences, while the keys and the values both represent the source sequences. Specifically, given queries (Q) and a set of key-value pairs (K, V), an attention function can be generalized to compute a weighted sum of the values (i.e., a context vector) for each query according to the relations between the query and the corresponding keys. By separately dividing Q, K, V into partitions, the MHA applies multiple independent attention functions (hence multi-head) to the partitions and extracts various context vectors (Ci):
where WC,and are learnable parameter matrices, h is the number of heads, i indicates the ith partition, and d is the feature size of Ki.
According to the inputs of the function, we differentiate two types of MHA:
Regarding the valid positions (or time steps) of a sequence to which the attention function is applied, MHA can be classified into another two categories:
For the Transformer, as shown in Figure 1a, the MHA units in the encoder layers and the first MHA unit in each decoder layer are intra-MHAs, while the second MHA unit in each decoder layer is an inter-MHA. It is worth noting that the intra-MHAs in the decoder are uni-directional (backward) due to the autoregressive decoding approach of the Transformer.
On the other hand, the FFN unit is composed of 2 fully-connected layers by which the input is projected onto a higher dimensional space followed by a rectified linear unit (ReLU), and then projected back to its original dimensions:
where W1 and W2 are parameter matrices, and b1 and b2 are learnable bias vectors. Alternatively, the fully-connected layers in the FFN unit can be replaced with 1-dimensional convolutional neural networks:
where * denotes the convolution operation. We refer to the two variants of FFN as fully-connected FFN and convolutional FFN. The sizes of all the parameter matrices are shown in Table 1.
|Computational Block
|Multi-head Attention
|hd × hd
|hd × d
|Fully-connected FFN
|hd × 4hd
|4hd × hd
|Convolutional FFN
|n × hd × hd
In practice, the MHA and the FFN both employ residual connections (He et al., 2016) followed by layer normalization (Ba et al., 2016). Moreover, since the two computational units process all positions of a sequence in a parallel manner without regard to the sequential order, Transformer-based models usually incorporate explicit position information, such as absolute positional encodings (Vaswani et al., 2017) and relative positional encodings (Shaw et al., 2018; Dai et al., 2019; Huang et al., 2019).
The BTC is built upon the Transformer encoder in a fashion similar to BERT, aiming to learn the bi-directional representations of the inputs. Specifically, the BTC learns the representations by employing two uni-directional intra-MHAs, one forward and the other backward, both followed by a convolutional FFN, as depicted in Figure 1b. This bi-directional approach makes a distinction from BERT in which the bi-directional intra-MHA is used. Although the technique of combining forward and backward intra-MHAs has been practiced in the fields of NLP and computer vision (Shen et al., 2018; Hossain et al., 2019), its effect on music data is still unexplored.
In contrast to the BTC, the HT retains the encoder-decoder architecture for the sake of integrating the chord change prediction into the chord recognition process. As shown in Figure 1c, the encoder and the decoder are the same as that of the Transformer, except that the intra-MHA of the decoder is bi-directional rather than uni-directional (backward). This difference results from the fact that the HT adopts a non-autoregressive framework (Gu et al., 2018), and hence can dismiss the backward constraint. Another difference is that the encoder of the HT has an additional output for the chord change prediction.
Comparatively speaking, the HT is more complex than the BTC in terms of model architecture and training techniques. In the aspect of model architecture, the HT has one additional inter-MHA in order to connect the decoder with the encoder. As a result, the HT has slightly more parameters than the BTC (around 4h2d2). In practice, however, the BTC consists of more repetitive layers than the HT (8 and 2, respectively), resulting in higher model capacity. In the aspect of training techniques, the HT includes a computational block called regionalization (not shown in Figure 1c) to pass the chord change prediction to the decoder; also, softmax-normalized layer weights are employed to compute the weighted sum of the outputs from all repeated layers.
Both the BTC and the HT process the input sequences at the frame level. That is, the intra-MHAs at the bottom of the models represent each frame with respect to the relations between the individual frames. However, the frame sizes (which may be on the scale of 100 milliseconds for audio and of a 16th note for symbolic music) are somewhat small for encoding harmonic content. Moreover, the MHAs access all the time steps of a sequence in parallel at the expense of knowing the sequential order. Although the BTC and the HT employed the absolute positional encodings of the Transformer in compensation, it was argued that the relative differences in position matter more for music (Huang et al., 2019).
We propose to improve the Transformer-based ACR models according to the two aforementioned aspects. First, we introduce intra-block self-attention (Shen et al., 2018), or intra-block intra-MHA, to the input of the models for learning localized harmonic features. Concretely, the intra-block intra-MHA unit splits a sequence into B blocks of equal length m and captures the local dependency within each block with the bidirectional intra-MHA:
where X(b) indicates the bth block of X, and (·) denotes a concatenation along the time dimension. In other words, the intra-MHA is applied to the B blocks individually for modeling the local context inside each of them. Second, we employ relative positional encodings (Dai et al., 2019) and positional attention (Gu et al., 2018) to enhance the model's knowledge of sequential order. In this way, the relative positional encodings enable the MHA to consider the pairwise relationships between its input elements, while the positional attention incorporates positional information directly into the attention process.
We apply the aforementioned techniques to the HT, and the architecture of the derivative model (denoted as HT*) is illustrated in Figure 2. The intra-block intra-MHAs (followed by convolutional FFNs) are added to the bottoms of the encoder and the decoder; the relative position encodings are introduced to all the MHAs; and the positional attention is employed in the decoder according to its original setting. In addition, the fully-connected FFNs of the HT are replaced by the convolutional FFNs in order to capture the adjacent information of their inputs before outputting to the next repetitions. From the perspective of the network topology, the combination of the intra-block intra-MHA and the bi-directional intra-MHA functions in a way similar to a convolutional recurrent neural network (CRNN) which captures local features with convolutions first and models long-term structure with recurrences thereafter (McFee and Bello, 2017; Micchi et al., 2020). We use m = 4 for the intra-block intra-MHA, and set the maximum relative position to the input sequence length. In the following section, we experimentally compare the BTC and the HT, and validate the improvement in the HT*.
To train the models for the chord recognition and the functional harmony recognition tasks, two corpora are used: the BPS-FH dataset and the Bach Preludes, where symbolic music and human-annotated RN labels are provided. The former includes complete first movements of the Beethoven Piano Sonatas (32 movements in total), and the latter consists of 24 preludes from the first book of Bach's Well Tempered Clavier. We use the BPS-FH dataset because it was used to evaluate the HT. In addition, we include the Bach Preludes as it has similar properties to the BPS-FH dataset (both comprise piano solos). We leave other datasets (e.g., the ABC dataset and the TAVERN dataset) for future work.
The analytic information of the Bach Preludes is transcribed into the tabular format of the BPS-FH for unifying the notation system of the two corpora. We additionally derive chord symbols from the RN annotations for the chord recognition task. In sum, there are 11478 labels in the BPS-FH, and 2615 labels in the Bach Preludes. All analyzed chords in the two corpora are categorized into 10 classes, as shown in Table 2. Figure 3 depicts the statistics of the annotated chords of the two corpora in terms of chord quality and degree. The statistics of chord quality show that major, minor and dominant seventh take the majority in both corpora, followed by minor seventh, diminished, and diminished seventh. And the degree distributions also manifest a similarity between the two corpora, in the sense that the top six chord degrees (i.e., 1,5,2,4,7,6) are almost the same.
|Major-Minor Mapping
|Major (M)
|Minor (m)
|Augmented (a)
|Diminished (d)
|Major Seventh (M7)
|Minor Seventh (m7)
|Dominant Seventh (D7)
|Diminished Seventh (d7)
|Half-diminished Seventh (h7)
|Augmented Sixth (a6)
The musical pieces in the repertoire are represented as binary piano rolls with the time resolution of one 16th note, resulting in sequences of 88-dimensional feature vectors. A sliding window of length 128 (equal to 32 quarter notes) with a hop size of 16 is applied to the piano rolls to generate the instances for recognition. For the chord recognition task, we use the maj-min chord vocabulary (including 24 major and minor chords plus an additional 'others' class which is excluded from evaluation). The mapping of the chord qualities is shown in Table 2. We choose this vocabulary rather than other vocabularies of larger size for two reasons. First, both the BTC and the HT were evaluated using the same vocabulary. Second, there is still room for improvement in ACR even using this relatively small vocabulary. For the functional harmony recognition task, we decompose the RN labels into two parts, i.e., the key and the RN, whose vocabulary sizes are 42 and 5040 respectively, as delineated in Table 3.
|Vocabulary Size
|21 tonics
|2 modes
|Roman Numeral
|9 primary degrees
|14 secondary degrees
|10 qualities
|4 inversions
We evaluate the BTC, the HT, and the HT* on the chord recognition and the functional harmony recognition tasks; and 4-fold cross validation is performed on each corpus. To create cross-validation sets, we naively assign a fold id to a piece according to the piece's id: fold_id = piece_id % 4. The training data are augmented via modulations (from 3 semitones down to 6 semitones up), leading to 10 times the original amount of data. As a result, the amounts of training and validation data are around (54320, 294) for the BPS-FH and (5380, 26) for the Bach Preludes. In addition, we train several variants of the BTC and the HT for the ablation study, and a CRNN for the comparison with the HT*:
The BTC-singleBi and the BTC-FC are employed to examine the effectiveness of the uni-directional intra-MHA pair and the convolutional FFN; the HT-noReg and the HT-noW are used to verify the contributions of the regionalization unit and the layer weights. As for the CRNN, we regard it as the benchmark for the HT* since they have a similar network topology. The architecture of the CRNN is modified from the network of Micchi et al. (2020), whose number of parameters is made comparable to the HT*. We set the number of repetitive layers to 2 for all models (other hyperparameters can be found in Table 1). During training, we set the dropout rate to 0.1; early stopping is applied once the model's performance stops improving on validation data for 10 consecutive epochs, and we report the best performance for evaluation.2
All models are evaluated in two aspects: 1) frame-wise chord recognition accuracy, 2) chord segmentation quality. The former shows the capability of a model to correctly predict chords at the frame level, while the latter assesses the predicted chord sequences from the perspective of chord segmentation. We utilize the directional Hamming distance (DHD) (Mauch and Dixon, 2010; Oudre et al., 2011) to evaluate the segmentation quality (SQ):
where Sn denotes the frames belonging to the nth segment of the annotated segmentation S, and Ŝn' denotes the frames belonging to the nth segment of the predicted segmentation Ŝ. The SQ value reflects the similarity of two segmentations, ranging from 0 to 1. The higher the value, the better the segmentation quality. In particular, a value of 1 indicates that the two segmentations are exactly the same.
Table 4 shows the overall performance of each model. For the BTC-singleBi, using single bi-directional MHAs instead of uni-directional MHA pairs appears to lower the recognition accuracy when the amount of data increases (the case of the BPS-FH) and when the complexity of the task increases (the case of functional harmony). However, this may result from the fact that the number of parameters in the model is half of that in the BTC. For the BTC-FC, the substitution of the fully-connected FFN for the convolutional one is harmful to the performance in most of the cases, because a fully-connected network only computes the weighted sum of its inputs and does not take into account the temporally adjacent information which helps relate local features to higher-level semantics (Ren et al., 2019).
|Chord Symbol Recognition
|Functional Harmony Recognition
|Roman numeral
|Bach Preludes
|Chord Symbol Recognition
|Functional Harmony Recognition
|Roman numeral
On the other hand, the HT surpasses the HT-noW in nearly all measures on the BPS-FH (while they are more or less comparable on the Bach Preludes), indicating that it might be beneficial to use information from all the layers of the network. Given that previous work has shown that different layers of a deep neural network tend to encode different types of information (Melamud et al., 2016; Belinkov et al., 2017), we believe that the employment of layer weights can increase the model's capability, especially when the encoder and the decoder of the HT are designated to different objectives (i.e., chord segmentation and chord recognition). Moreover, the HT outperforms the HT-noReg in four out of the five measures on the BPS-FH and in three measures on the Bach Preludes, validating the employment of the regionalization unit.
In comparison with the BTC, the HT appears to be more competent for it is more accurate in eight out of the ten measures (four from each corpus). In particular, the worst HT variant even outperforms all the BTC variants in terms of chord segmentation quality, showing that the concurrent estimation of harmonic changes benefits the outcome of chord segmentation.
It can be seen that the HT* substantially outperforms the CRNN benchmark in all cases. Although CRNN-based networks are known for their ability to jointly learn local features and model sequences, the employed CRNN fails to compete with the HT*. In comparison to the HT, the HT* obtains a consistent gain in overall performance, and the boost in segmentation quality is especially notable, validating the proposed improvement on learning the localized and position-related information. More strikingly, the HT* outperforms all the other models in comparison and sets new records for the current experiments. An examination into the multi-head attention mechanism reveals that the attention heads capture various concepts of musical harmony. As depicted in Figure 4a, two attention heads in the intra-MHA of the decoder display distinct attention patterns: one head (on the left hand side) appears to be aware of the chord regions, hence blocks of the attentive regions are formed along the diagonal of the attention map; the other head emphasizes more on the harmonic changes, resulting in several vertical lines traversing the attention map. We also observe different attention patterns in the inter-MHA of the decoder. As illustrated in Figure 4b, there are different block patterns signifying the harmonic structure of the input segment on the two attention maps. More specifically, the attention map on the left hand side reveals that the HT* is capable of recognizing the boundaries between the chords (as there are many darker lines around the positions where the chords change); while the attention map on the right hand side indicates that the tonic chord (F:m) and the dominant chord (C:M) put emphasis on different parts of the encoder sequence (see the red regions on the attention map).
To summarize, the performance of the Transformer-based model can be advanced by employing the intra-block intra-MHA and by enhancing the contextual information. Additionally, it is observed that the MHA units in the model can capture various harmonically meaningful characteristics of music, yielding prominent performances on both the chord recognition and the functional harmony recognition tasks. However, it should still be noted that these results are obtained by evaluating the model on only two piano solo corpora. Experiments using more diverse data are required for a more comprehensive assessment of the model.
The chord recognition accuracy is a simple and universal approach to evaluate ACR systems, but it is only part of the story. With formulating chord recognition as a classification problem, ACR systems using deep learning approaches are restricted to a single ground truth. In practice, however, several different annotations for the same harmonic entity are often equally viable due to the analytical essence of the recognition process, and the subjectivity in the ground truth will affect the evaluation of ACR systems (Ni et al., 2013). It is possible to take into account the relations between different annotations or predictions, and develop new loss functions for optimizing a deep learning model (Carsault et al., 2018).
Chord segmentation quality is another useful criterion for assessing frame-based ACR models, as the chord progressions obtained from these models should not be fragmented. Considering that chord boundary detection and chord recognition are intertwined problems, the ACR task may benefit from other advanced segmentation strategies, such as hierarchical representation (Chung et al., 2017), segmentation gates (Wang et al., 2018), and segment-directed attention (Hou et al., 2020). Instead of the frame-wise chord recognition, it is also worthwhile to explore methods for recognizing chords at a higher hierarchical level (Korzeniowski and Widmer, 2018).
As for functional harmony recognition, the experimental results suggest that there is still a long way to go to automatically produce RN analyses of sufficiently high quality. As functional harmony recognition relies heavily on the semantic content of music, it would be beneficial to leverage more high-level musical elements (e.g., metrical position and phrasing) for data representation. Moreover, designing reasonable output vocabularies is also important. Chen and Su (2019) predict the components of each RN label individually, while in the current experiments, they are combined into a single RN. The former reduces the output size of each component, but makes the components somewhat independent of each other; the latter alleviates the dependency issue but enlarges its output vocabulary size. Therefore, it is required to mediate between the two approaches. In addition, RN analysis is a fundamental tool for music theorists to uncover the tonal structure of music; hence human-in-the-loop approaches to functional harmony recognition are also valuable (Micchi et al., 2020).
Finally, currently available datasets for symbolic ACR are usually small and homogeneous. More work devoted to creation of symbolic datasets is thus welcomed. More diverse corpora would enable researchers to develop standard benchmarks for a more comprehensive and systematic evaluation.
We systematically studied two Transformer-based ACR models in terms of model architecture, chord recognition accuracy, and chord segmentation quality. Furthermore, we also examined how the major components of the two models affect the overall performance. Based on one of the two models, we further improved the performance by leveraging the local context and the positional information of input music. In addition, experimental results showed that multi-head attention has the potential to capture various harmonically meaningful features in the scenario of ACR. We consider that attention-based models are promising for recognizing chords, not only because they have yielded fruitful results in various sequence modeling tasks, but also the attention mechanism is capable of accessing sequences in a relatively comprehensive way without being constrained by the receptive field of convolutions or by the sequential order of recurrences.
We have put emphasis on ACR in general and on the deep learning-based systems for symbolic music in particular, due to their potential to scale up the harmony-related annotation data. This enables us to investigate harmony from a macro perspective. With such a process, researchers will be able to provide a different insight into harmony than may be observed otherwise with a small number of musical instances. We hope that our research will draw more attention to symbolic ACR and encourage the MIR community to build datasets and develop techniques for symbolic music.
1The implementations of the BTC and the HT can be found at https://github.com/jayg996/BTC-ISMIR19 and at https://github.com/Tsung-Ping/Harmony-Transformer, respectively.
2The implementations of the models for evaluation can be found at https://github.com/Tsung-Ping/Harmony-Transformer-v2.
We thank the editor and the anonymous reviewers for their insightful comments.
The authors have no competing interests to declare.
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Deng, J. and Kwok, Y. (2017). Large vocabulary automatic chord estimation with an even chance training scheme. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), pages 531–536.
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text: European-born Stephan Jay Rayon grew up in Canada and moved to New York City some 20 years ago. A self-taught artist and graphic designer who fuses European and North American heritage to form a visual language that transcends cultural labelling, he is perhaps best known internationally for his work for the Japanese fashion label Hysteric Glamour. Viewers are drawn to the sensuality, theatrics and simplicity of the sleek paintings of Jay Rayon. As a child, Jay Rayon found his passion in art through cinema. The carefully staged scenes on the big screen generated more interest for him than could a painting or a drawing. One can find the Pop aesthetics of cinema and fashion in his work as well as absurdism and unrealism.


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Nigeria Entertainment News
See The Reason Why Don Jazzy Is Weeping
Marvin Record boss, Michael Collins Ajereh a.k.a Don Jazzy, who has not hidden his crush on famous blogger Linda Ikeji, `weeps' over the news of her engagement.
Don Jazzy posted a picture on his instagram @donjazzy as he was being consoled by some friends over his dashed dreams of making Linda his `wife'.
According to some online reports on Wednesday, Linda's ex-boyfriend recently relocated to Nigeria where the lovebirds decided to rekindle their love affair and got engaged.
He also wrote with a crying emoji's: Kai Linda is engaged.. It is finished.''
Before the news, Don Jazzy had made several moves and also 'proposed' to her (Linda) on social media and with various posts on his instagram handle; one of such was a recent photo of Linda with the caption "When you re fine you re fine abeg."
Peter Okoye of the defunct PSquare, offered to sponsor his wedding with Linda if Don J made good his promise.
Although Linda, herself have not confirmed the report, however, Sandra, Linda's sister confirmed the good news on her instagram handle on Wednesday.
Sandra wrote @sandra_Ikeji: congratulations @officiallindaikeji So habby sweet sis! This tells me that sometimes the wait is worth it.
Can't wait to plan your special day @eventsbysi are you readyyyy #itsagetaway.
The news was also confirmed by another sister Laura, @lauraikeji who wrote, "Congrats Linlin @officiallindaikeji."
Naija News had reported earlier that Linda Ikeji and and her heart throb would be walking down the aisle as she recently got engage to her Ex-boyfriend.


text:
Argyle High School has enjoyed a fine athletic tradition in its short history, and no sport better exemplifies the district's commitment to excellence than basketball.
With the Argyle football team advancing to the regional quarterfinals this season, and the volleyball team going four rounds deep in the playoffs, the Eagles hoops squads have had to wait for the proverbial limelight.
But when January and February roll around and the basketball teams are right in the thick of district 9-3A competition, Argyle will very likely prove once again why its teams are always a threat to go deep into the post-season, and occasionally, rival for a state championship.
"It's really surprising when you consider how young the school is," Argyle head girls coach Steve Schmidt said. "That's the most impressive thing. I started coaching in the panhandle, where some of those schools have been around for a century, and I can tell you, it usually takes some time to develop a tradition.
"Argyle has just been unbelievably blessed with good hard-working kids, very supportive parents who give their kids the opportunity to work outside the school day on developing their skills, and we're in a good basketball area; so the competition is also a big part of the reason why our program has come a long the way it has—you either sink or swim."
Argyle has only competed at the varsity level in basketball for the last seven years, but in that time has made two trips to the state championship game on the girls side.
In 2006, the Argyle girls basketball team defeated Wall 51-33 to win the state 2A championship, and had its star player from that squad, Ally Clardy, earn a full-paid basketball scholarship to Oklahoma State University.
Last season, Argyle lost in the 3A state championship to Robinson High School by a score of 49-33.
The Argyle girls came into the season as the No. 3 ranked team in the state according to a poll on TexasGirlsBasketball.com, and Schmidt said, while football and volleyball do get a lot of the attention at Argyle, he believes that benefits the basketball programs, as well.
"I wouldn't say that we are overshadowed," Schmidt said. "We just have to sit back and wait our turn. We don't get the two-a-day workouts before school starts, and I can just imagine how great that would be if we had the time to bring our kids in and spend several hours a day getting them ready for the season. But that's just not the way our season falls.
"I think that the winning in football and volleyball also pays off dividends in volleyball. I really think the kids just believe that they can win. They believe that if you work hard enough and play as a team, you're going to be successful."
The Argyle girls earned its first playoff berth in 2003, losing in the opening round, and have been at least to the regional tournament four years since then.
Schmidt has also had six of his players go on to play college basketball, including two Division I players, in Clardy, and Brooke Shepherd, who is playing at Northwestern State University in Louisiana.
Argyle senior guard Claire Pettibon averaged 10.5 points per game last season for the Lady Eagles, and said playing basketball at Argyle is very special for her and her teammates.
"I just love the camaraderie we have," Pettibon said. "Everyone just really loves the sport and goes hard, and it is just really fun to play in that environment."
On the boys side, Argyle came into this season as the 11th ranked team in the state according to a pre-season poll on TexasHoops.com, and wrapped up the 2008/2009 campaign with a 48-46 loss in the regional quarterfinals to Dallas Madison High School.
The boys team has enjoyed its fair share of success in a short period, as well, as it advanced three rounds in the playoffs in the 2006/2007 and 2007/2008 seasons, before losing to Brownfield on both occasions.
Caden Dickerson, who went on to play basketball at the collegiate level at Western Kentucky, was named the districts' Most Valuable Player in the 2006/2007 season and earned a spot on the all-state team.
The Argyle boys lost in the 2A state championship in the 2003/2004 season to Shallow Water by a score of 47-45 in overtime, and have been to the playoffs every year except for 2006 since then.
Argyle boys basketball coach John King could not be reached for comment on this story.
Pettibon said the tradition of the basketball program at Argyle is something that is ingrained in the players from the moment they arrive at the school and said every team feels an obligation to carry that on to the next year's team.
"Coach Schmidt is really big on tradition," Pettibon said. "He always gets us together in meetings with underclassmen, and we talk about our past years and how we need to carry on the winning tradition."


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Soil fertility refers to the capacity of a soil to support the production of crops and livestock. A fertile soil can support optimal plant growth from seed germination to plant maturity. In other words, It is the capacity of soil to supply sufficient quantities and proportions of essential chemical elements (nutrients) and water required for optimal growth of specified plants as governed by the soil's chemical, physical and biological attributes.
On the other hand, soil productivity is the capacity of the soil to produce crops with specific systems of management and is expressed in terms of yields.
NB: All productive soils are fertile, but not all fertile soils are productive.
The Reasons could be either one or two or interplay of many constraints. These are water logging, saline or alkaline condition, adverse climate etc. Under these conditions, crop growth is restricted though the soil has sufficient amounts of nutrients.
Soil health refers to the physical, chemical and biological fertility of soil. It is therefore, an intersection of the three approaches or holistic approach to enhancing the overall fertility of the soil.
Soil fertility indicators refer to a set of measurable and verifiable parameters that distinguishes fertile soil from infertile ones.
Fertile soil indicators include:
- Colour- largely dark coloured soils indicate a good amount of organic matter and humus. However, it is not a full proof indicator since some dark coloured soils belonging to the Akuse series are dark but not necessarily fertile. Other indicators include the following:
- Texture: sandy soils tend to be less fertile due to their in ability to hold nutrients and water.
- Less availability of rocks and stones
- Low frequency of watering/irrigation
- Visible cracks on the soils during the dry season
- Good crop performance, like maize, millet etc without the use of fertilizers, manures and crop residues,
- Presence/growth of plants that survive during the dry season
- Presence of green vegetation during the dry season
- Presence of wild sisal and many ever green trees/vegetation
Indicators of infertile soil:
- Poor crop performance even with the application of fertilizers or manures
- Occurrence of red or light coloured sandy soils
- Poor natural vegetation even where water is not limiting
- Cyperus rotundus seen all over, for instance,
- Compacted soils
- Prominence of coarse sand depositions on the landscape
- Stunted plants even during the raining season when water is adequate
- Fast drying up of soils after rains or irrigation
- Presence of white spots/patches on the soil surfaces
- Presence of salts or salt patches on the soil surface.
- Presence of very coarse sands, gravel and stones on the landscape.
Soil fertility measurement begins with sampling of the soil to determine some key physical, chemical and biological parameters. There are standard methods or procedures for taking soil samples for these determinations.
The general steps include the following:
- Collect soil cores to the required depth
- Collect enough cores to make the representative samples.
- A sample should not represent too large an area.
- Collect soil samples to represent different soil or topography types.
- Provide samples information on the form being submitted to a laboratory.
In picking soil samples for fertility measurement, the following should be observed:
- Avoid waterlogged areas
- Avoid recently fertilized, either by organic or inorganic fertilizer.
- Avoid disturbed areas
- Avoid recently burned locations
The fertility of the soils in the tropics and sub-tropics, which includes major soils of Ghana's agroecological zones, have both inherent and externally induced fertility challenges.
The inherent factors of the low fertility of these soils are as follows:
- Type of parent materials
- High amounts of rainfall (precipitation)
- Low soil organic matter (SOM) or carbon
- High decomposition rate of SOM
- Low external inputs of fertilizer
- Continuous cropping
These factors are briefly expanded as follows:
- Type of parent material:
The kind of parent material leads to dominant mineral that forms the chemical interphase for most soils and nutrient retention and availability. The tropics is one of the oldest landscape and coupled with hot-humid climate, weathering is intense leading to abundance of low activity clay minerals such as Kaolinite, goethite, gibbsite, etc. which have low cation exchange capacities, hence, the low CEC observed in tropical soils. Cation exchange capacity here refers to the amount of cations that the negatively charged surfaces of soil colloids can hold onto and exchanged between the soil solution and the colloidal surfaces. The lower the amount of negative charges surfaces on the soil surfaces, the less the amount of positively charged nutrients the surfaces can hold, hence, the term low cation exchange capacity (low CEC).
- High amount of rainfall:
The tropics and sub-tropics are characterized by heavy and erratic rainfall pattern which lead to leaching of basic cations such as calcium (Ca), Magnesium (Mg), Potassium (K) and sodium (Na), which results in the accumulation of Al, Fe, Mn and Zn, hence, soil acidity.
- Low soil organic matter contents:
This is as a result of rapid decomposition aided by the high ambient temperature. In addition, the high rainfall amounts also aid this decomposition process.
The external factors are as follows:
- Continuous cropping:
Sub-sahara Africa has agricultural practices that tend to deplete nutrient resources of the soil. Some of these practices are nutrient mining through harvest practices that leave very little behind for incorporation into soil, bush burning and the low use of external inputs such as manures and fertilizers.
- Leaching:
During rainfall water that percolates/ sinks down through the soil pore spaces carry with it important soil nutrients that are moved beyond the reach of plant roots resulting in low availability of nutrients around plants roots for growth and good crop yield. However, the process of leaching of nutrients beyond plants roots zones is not accelerated by rainfall alone. Excessive amounts of irrigation water also lead to the acceleration of leaching.
- Erosion:
The excessive amounts of rainfall at ago, meeting low soil percolation or infiltration results in surface run off. This surface run off carries with it large and small soil particles which hold on its surfaces soil nutrients. In addition, the smaller soil particles, which tend to play key roles such as porosity, aeration and increase in water holding and hydraulic conductivity leading to improved soil fertility are largely affected in this process thereby reducing the fertility of the soil in the process.
- Bushfires:
These destroy the soil as well as the organic matter much needed to build the soils' resilience against soil degradation factors such as direct raindrops that initiates soil dispersions and facilitates erosion. In its wake, bush fires accelerate not just organic matter loss, but also nitrogen and sulphur through a process of volatilization. The direct heat of burning may reduce the population of microorganisms in the soil and affect the soil texture and structure.


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Starling Bank provides a mobile banking application and a debit card that enable users to manage their finances.
Starling Bank is a digital, mobile-only challenger bank based in the United Kingdom, operating current accounts, and business banking. Headquartered in London, Starling Bank is a licensed and regulated bank, founded by former Allied Irish Banks COO, Anne Boden, in January 2014. Since its founding, it has received over $300m of funding. The company received its banking licence from the Prudential Regulation Authority and the Financial Conduct Authority in July 2016.


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As your IT partner we strive to add value to your business through technology solutions that help you realize a measurable return on your technology investment. Let us show you new functionality that can lower your costs and expand the foundation of services you provide.
Our highly successful track record demonstrates our ability to understand your business and seamlessly integrate with your project team. As our client, you have access to more knowledge through our individual and accumulated experience. Because we have a strong sense of integrity, pride and ownership in our work, we bring a winning perspective to every job and can help you achieve better results, even on your toughest challenges.


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ohyeahjay· Registered
140 Posts
Discussion Starter · #1 ·
Did not see a discussion topic but for anyone interested I replaced the five interior lights with pure light (5000k) HP3 style bulbs from Diode Dynamics. Pretty straight forward change out. The only minor pain was taking the rear overhead handles out but nothing serious. I forgot to take a before and after but here is the front. If anyone wants the rear or the trunk just let me know.


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HMD Global recently has announced that the company will be hosting an event in Moscow on 29 May. The existence of this event has actually been known for quite sometime and of course, users can expect to see a number of new Nokia Mobile products to be announced over there.
This was confirmed by the company's own Chief Product Officer, Juho Sarvikas although it is not clear what these new products going to be since Juho only referred to them as "new stuff" in a recent tweet. While HMD Global also includes the #ChargedUp hashtag in the tweet that carried the official confirmation of the Moscow event, there is no other clear indication on what the company will reveal at the launch event.
What's next in the #Nokiamobile story? Find out on Tuesday 29th May. It's time to get #ChargedUp. pic.twitter.com/UUwVeBM3Pj
— Nokia Mobile (@NokiaMobile) May 25, 2018
Then again, we expect the global version of Nokia X6 which was revealed few weeks ago in China to make an appearance at the Moscow event. There is also a recent rumor regarding the revival of Nokia N-Series although nothing followed through from it.
Let's also not forget about the rumored Nokia 10 as well which is said to come with five cameras. With nothing is certain at this moment, we guess it is wise to wait till 29 May and see what HMD Global have to show to the world in Moscow.
(Source: Nokia Mobile, Juho Sarvikas)
Follow us on Instagram, Facebook, Twitter or Telegram for more updates and breaking news.


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Representative Cases
Estate of Decedent v. Hospital and General Surgeon 2017 Umatilla County
TCNF trial attorney Cynthia Newton obtained a confidential 6-figure settlement on behalf of the four adult children of 62-year-old retired handyman who died following an attempted dilation of a colon blockage after 6 days' conservative in patient treatment. During the colon surgery the patient suffered cardiac arrest and died despite heroic efforts by the hospital's Code Blue team. The heart attack was due to a condition known as pulseless electrical activity, in which the heart does not receive adequate return blood supply through the venous system, so it beats but cannot pump blood to the body and eventually arrests. Air pressure introduced as part of the surgery slowed the return of venous blood and stalled the patient's heart.


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Ferb of "Phineas & Ferb" has joined the cast of "Game of Thrones."
Thomas Brodie-Sangster, the voice of the Disney Channel character, will appear in "Game of Thrones" Season 3 as Jojen Reed.
Thanks to HitFix's Alan Sepinwall for bringing the fact to our attention.
Romantic comedy fans may also recognize Brodie-Sangster as Sam from "Love Actually."
See a split of Ferb and Jojen below.
"Game of Thrones" Season 3 premieres Sunday, March 31 at 9 p.m. ET on HBO.


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Coordinator Shen Yee Ku
Hope Hobart
For more information contact us!
Service Time: Sunday 11.30am (fortnightly)
Email: [email protected]
Website: http://www.facebook.com/hopehobart.christianchurch
Due to the nature of the current COVID-19 situation, times and locations may differ from what is listed. Please contact the centre that you wish to attend prior to Sunday in order to receive the most current details.


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A render target is a buffer where the video card draws pixels for a scene that is being rendered in the background. It is used in different effects, such as applying postprocessing to a rendered image before displaying it on the screen.
width - The width of the renderTarget.
height - The height of the renderTarget.
options - (optional object that holds texture parameters for an auto-generated target texture and depthBuffer/stencilBuffer booleans. For an explanation of the texture parameters see Texture. The following are valid options:
wrapS - default is ClampToEdgeWrapping.
wrapT - default is ClampToEdgeWrapping.
magFilter - default is LinearFilter.
minFilter - default is LinearFilter.
generateMipmaps - default is false.
format - default is RGBAFormat.
type - default is UnsignedByteType.
anisotropy - default is 1. See Texture.anisotropy
encoding - default is LinearEncoding.
depthBuffer - default is true.
stencilBuffer - default is false.
Creates a new WebGLRenderTarget
The width of the render target.
The height of the render target.
A rectangular area inside the render target's viewport. Fragments that are outside the area will be discarded.
Indicates whether the scissor test is active or not.
The viewport of this render target.
This texture instance holds the rendered pixels. Use it as input for further processing.
Renders to the depth buffer. Default is true.
Renders to the stencil buffer. Default is false.
If set, the scene depth will be rendered to this texture. Default is null.
Sets the size of the render target.
Creates a copy of this render target.
Adopts the settings of the given render target.
Dispatches a dispose event.
For more info on how to obtain the source code of this module see this page.


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High-end automotive performance is combined with the ultimate in passenger space and comfort in the new Mercedes-Maybach Pullman.
Mercedes-Maybach's proud history of building vehicles that offer exclusivity and a first-class travel experience is brought to the forefront with the latest edition to its Maybach fleet.
With a host of high-end comforts fitted as standard, the vehicle lives up to modern expectations of what constitutes maximum exclusivity and luxury for Middle Eastern customers.
Available through Emirates Motor Company, authorized distributor of Mercedes-Benz in the Emirate of Abu Dhabi and the flagship company of ALFAHIM, the Mercedes-Maybach Pullman is the definition of safety, style and performance. The 5.6 tonne saloon car can reach speeds of up to 160kmph thanks to its twin turbo V-12 engine.
A favourite of royalty, government ministers, heads of state and high net worth individuals across the globe, the new Mercedes-Maybach Pullman's size is a sign of its special status.
At 6.5 meters-long, it has enough room for a generously proportioned and tastefully appointed club lounge in the rear.
The chauffeur-driven limousine's VIP passengers sit in two executive seats facing the direction of travel while two other passengers sit facing backwards.
All of the passengers enjoy the most legroom of a car of this type while the extra headroom - the Mercedes-Maybach Pullman is more than 100mm taller than a Mercedes S-Class - means they can get in and out with the greatest of ease and comfort.
The two executive seats can be adjusted to a unique level of comfort. The backrest can be calibrated to a maximum angle of 43.5 degrees while the footwell and seat both remain unchanged. An upright position of 19 degrees allows for a relaxed working space.
The Mercedes-Maybach Pullman does not compromise on the comfort of passengers, with the interior adhering to the strict principals of style and luxury that has made the Maybach limousine marque so popular in the region and across the world.
The interior is upholstered in the finest quality leather throughout as standard, including the headliner, door frames and seat consoles.
As one would expect of a Maybach, there are three analogue instruments in the headliner informing passengers in the rear of the outside temperature, speed and time.
Chris Edwards, General Manager of EMC Passenger Cars said: "The Mercedes-Maybach Pullman is the pinnacle of automotive luxury.
"As well as spacious seating in a first-class level of comfort, the limousine is the embodiment of exclusivity at the highest level for high-ranking passengers.
"The Mercedes-Maybach Pullman offers complete comfort along with the stunning performance that is expected of a high-end luxury vehicle. Its popularity is testament to the vehicle's ability to meet the needs of some of the most discerning customers all across the globe.
"EMC is delighted to offer the Mercedes-Maybach Pullman to its customers across the UAE."
Dr. B.R. Shetty, the Founder and Chairman of BRS Ventures, UAE Exchange and NMC Health, was among the first customers to purchase a Mercedes-Maybach Pullman when the vehicle was released in the UAE.
He said: "The Mercedes-Maybach Pullman immediately appealed to me as it offered high levels of luxury, comfort and safety, especially since I spend a lot of time in my car traveling across the beautiful UAE.
"Mercedes-Maybach is renowned for creating high-end vehicles that combine exceptional performance with state-of-the-art technologies and superb comfort. The Pullman is a natural continuation of this proud tradition. There really is nothing else like it on the road.
"The Mercedes-Maybach Pullman can double as an extension of the office, allowing passengers the space and facilities they need to work effectively on the move, while also providing first-class luxury to help you unwind and relax in total comfort."


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Inspiring Educators Who Inspire Future Leaders
This guest post by Jim Cordery was simply too good to pass up so I am sharing it with all of you. It resonated with me because it talks specifically about leadership in connection with teachers in a way that is rarely seen. To the leader in all of us – enjoy! tlb


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More details on wild Massillon police chase that ends with three arrests
MASSILLON Officials are reviewing camera footage from a multi-jurisdictional pursuit after a driver fled from police during a traffic stop Tuesday night.
Three people were arrested and could face additional charges, Massillon Lt. Mike Maier said.
The driver of the vehicle, Joshua C. Leach, 40, of Barberton, was charged with felony fleeing, falsification and failure to comply with officers. Andrew Taylor, 35, of Akron, was charged with aggravated possession of drugs and obstructing official business. Taylor, who owned the vehicle, was a passenger.
The chase began after Massillon Officer Aaron Franklin observed a reckless driver and initiated a traffic stop along Southway Street SW near Coolidge Avenue SE.
When Franklin approached the vehicle, the driver put the vehicle in gear and sped off nearly running over the officer, Maier said.
The front passenger, Craig Blackshear, 35, of Canton, bailed out of the fleeing vehicle.
Blackshear was charged with felony having weapons while under disability, carrying a concealed weapon and defacing a firearm. He was transported to Tuscarawas County, where he was wanted on a warrant.
Another Massillon officer, who was conducting an unrelated traffic stop in the area, pursued the fleeing vehicle, Maier said.
The pursuit continued along Southway toward the Harrison Avenue area of Canton, where the driver circled the area, he said, before heading onto Shepler Church Avenue and to Interstate 77. Ohio Highway Patrol joined Massillon during the pursuit.
Maier said the driver attempted to strike oncoming traffic head on, veering into the path of vehicles. No vehicles were struck and no one was injured, he said.
During the chase, Canton police officers shut down ramps along the I-77. The Canton officers told Massillon PD the vehicle and person fit a description of person wanted in a Canton shooting, Maier said.
It is unclear at this time if any of the people in the vehicle were involved in the shooting, he said.
The driver exited the highway at the Whipple exit, which is closed for construction, heading down the grassy the median and onto the closed ramp, Maier said.
Jackson Township police found the abandoned vehicle behind Movies 10, 6284 Dressler Road.
A woman who was a passenger along with Taylor made their way to nearby Table Six Kitchen and called dispatch.
The woman was not charged, Maier said.
Leach was found in the restroom of Quaker Steak & Lube and arrested.
"A lot of stuff happened last night," Maier added. "We don't normally pursue but after he almost struck an officer and was possibly a suspect in a shooting we went a little bit further than we normally would go. It all worked out. No one was injured."
Reach Amy at 330-775-1135 or email@example.com
On Twitter: @aknappINDE


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If you already making Online Betting on sports but not making serious money, you are making some of the more common mistakes that are literally draining your bankroll. These are a few of the most common sports wagering mistakes you make and need to eliminate from your game.
Setting Online Betting Goals
If you didn't take the time to make a goal for the day, how are you going to know when to take your winnings off the table before a cold streak wipes you out? Decide before you make a bet what you want to win for the day, then stick to the plan and get the money out of there. The best type of goal is a percentage goal, so decide you'll quit after winning ten percent of your bankroll, and pull the money off the table soon as you hit that goal.
Eliminate Emotions from the Game
Never place bets based on gut feelings. There are too many things that can go wrong for you to simply be selecting teams at random. Worse, stop betting teams you are emotionally connected to, these teams will literally wipe out your winnings in the long run. If you have to bet the team from your state, make friendly bets with your friends, don't risk your sports betting bankroll on these gut feelings.
Investing in Analytics Software
Set aside some time each night or early morning to study and crunch numbers. If you are listening to the chatter of sports analysts, you are going to go broke because not only are they never accountable, they get paid even when they lose. While you are still early in this journey, spend a couple dollars on software that can crunch the numbers for you after you have done a little research. The software does the grunt work, and you simply apply the numbers to the games and spot opportunities to win more frequently.
Lowering Your Bet Amounts
Look at the size of your bankroll compared to how much money you risk on each bet. Never change the bet amounts, they should always be only two percent of your bankroll. Chances are pretty good that you are betting more like twenty-five percent on each wager, and then wondering why you are broke at the end of the week. Drop to a much lower bet amount and stick to the same amount if you want to start seeing results. If you are betting too much, you could wipe out the bankroll after just a few loses. Betting less keeps you in the game longer and gives you the chance to grow that bankroll steadily.
Stay focused and work through this list one at a time and don't let one little setback get you discouraged because things will improve soon. There will be days that you make mistakes, but since you are pulling the plug at a certain limit, you are less likely to dump your entire bankroll.


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alarms, Christ's return, lunar eclipse, people of light, sleep walking, sleeping, slumbering, time is up, wake up
I hear there was a lunar eclipse Wednesday morning. I wouldn't personally know because I slept right through it. I am usually up at that time (it was 5:30 in my time zone), but that morning I slept right on without a care in the world. I thought it would be awesome to wake up and see this remarkable phenomenon of God's creation, but obviously I didn't care enough to set an alarm. In fact, I had planned on waking up my youngest son so we could experience it together. He was excited, but didn't care enough to set his alarm either. Apparently, we both preferred sleep.
As we went about our day, I wondered how often I have missed God's voice because I was simply asleep. How often have I missed a great work of God because I chose slumber instead?
Physically speaking, there have been many times throughout my life when I had big plans for morning devotions, but due to late night activities, chose to sleep instead. Lamentations 3 tells us that His mercies are new every morning and I can't help but wonder what new mercy I missed for those days simply because I viewed sleep as more important.
Spiritually speaking, I know there have been times when God has tried to speak to me, but I was totally asleep. In my daily Christian walk, I have often gone through the motions with no real destination in mind just as a sleep walker would do. I have mistakenly thought because I was checking all my "good girl" boxes I was on the right path. In reality, I was asleep at the wheel.
Psalm 86:8 says, "Lord, there is no one like You among the gods, and there are no works like Yours." I must ask myself how often I have been asleep instead of viewing the wonders that God has created around me, in me and through me.
When I am asleep spiritually I am not being wise. I am missing out on God's plan for that time in my life. Ephesians 5:14 says, "Awake, sleeper…Therefore be careful how you walk, not as unwise men, but as wise, making the most of your time…" I am not being sensible when I am asleep. My time is short and I am missing the opportunities that God has for me that day. I need to WAKE UP!
You might remember that I was not the only one who missed out on the eclipse. I also failed to awaken my son. Romans 13:10-12 tells us, "Love does no wrong to a neighbor; love therefore is the fulfillment of the law. And this do, knowing the time, that it is already the hour for you to awaken from sleep; for now salvation is nearer to us than when we believed. The night is almost gone, the day is at hand. Let us therefore lay aside the deeds of darkness and put on the armor of light."
This verse reminds me that not only do I need to wake up for myself, but also for those around me. Since I chose to sleep through the eclipse, I caused my son to sleep through it as well. Yes, he could have set his own alarm, but he trusted me to help him see it.
How many times have I missed an opportunity to tell my neighbors about Jesus because I was asleep? My own family? How many times have I been numb to the realization that JESUS IS RETURNING and I only have a few days to share Him with others? God has entrusted my neighbors to me to help them find their way to Him, but I find myself tired and wanting more sleep. I am not just drowsy to that fact. I am sound asleep. I need God's Holy Spirit to wake me up!
I Thessalonians 5:2-6 says, "For you yourselves know full well that the day of the Lord will come just like a thief in the night. While they are saying, 'Peace and safety!' then destruction will come upon them suddenly like birth pangs upon a woman with child; and they shall not escape. But you, brethren, are not in darkness, that the day should overtake you like a thief; for you are all sons of light and sons of day. We are not of night nor of darkness; so then let us not sleep as others do, but let us be alert and sober."
We do need to be aware from these verses that even when we are awake, others around us may still be sleeping. I don't know about you, but my experience tells me that people who are sleeping do not like to be disturbed. We need to remember that people will not like us waking up and they for sure will not like us trying to wake them up, (Christian and non-Christian). No matter what though, we need to be about our Father's business. We need to wake up ourselves and then help others do the same. Remember, they are groggy from sleep. That means they are most likely grumpy. Keep trying, keep praying and keep sounding the alarm UNTIL they wake up fully.
It may seem like a desperate situation around you right now. In every direction you may only see people asleep. Rest assured God can handle it. We see no more desperate situation than Ezekiel in the valley of dry bones (Ezekiel 37). Those dead, dry bones were no match for the Holy Breath of God. Those brittle, dry, undernourished, sleeping individuals became a mighty army through Ezekiel doing what God called him to do. He was commanded to speak over those bones and God used him to revive the sleepers. So, do not be discouraged. Even if you are the only one awake in your surroundings, keep pressing forward. Keep listening for God's voice amongst all the snoring and obey His every prompting. You never know when God's Spirit will break forth, the bones will start rising and a mighty army will prevail.
"Wake up, O sleeper!"
http://Biblical Devotions – Join the Faithful Bloggers!
Please visit the website at – Seeking Hearts


text: Beyond Antibiotics 2009-01-202009-01-20 Michael A. Schmidt, Ph.D. Author: Michael A. Schmidt, Ph.D. Publisher: North Atlantic Books ISBN: 1556437773 Category: Health & Fitness Page: 460 View: 116 GET EBOOK→ At a time when the numbers of emerging infections and antibiotic-resistant bacteria are rising sharply, the supply of new antibiotic drugs has been steadily decreasing. In addition, many health providers have failed to consider that our bodies are cloaked in a blanket of bacteria so pervasive that the bacterial cells outnumber our "human" cells by a factor of ten. In short, we are living in a microbe's world and cannot ignore the very real potential for untreatable serious infections. In this timely book, Dr. Michael Schmidt proposes we focus on strengthening ourselves by thinking of our bodies as a "human-microbe hybrid." This requires taking action to raise our defenses, while preserving the integrity of the microbial elements that live on and within us. Drawing on the latest research from several scientific fields, Schmidt presents a strategy of medicine that can be used to build and balance our system of immune defense and repair. He offers a set of general recommendations that can easily be used to tailor programs to individuals seeking to support health maintenance, prevent illness, fight active acute or chronic infections, and foster faster recovery from infections.


text: This final issue of The Walking Dead is signed by Robert Kirkman and certified by CGC at 9.8!! The item "THE WALKING DEAD 193 FINAL ISSUE 1st PRINT SIGNED BY ROBERT KIRKMAN CGC 9.8" is in sale since Wednesday, November 20, 2019.This item is in the category "Collectibles\Comics\Modern Age (1992-Now)\Horror & Sci-Fi". The seller is "comics2cyclists" and is located in Westminster, Colorado. This item can be shipped worldwide.


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Man Has Skin "Torn Off" In Fatal Shark Attack
A terrifying story shared by Fox News details the violent death of a California man who was killed in Hawaiian waters after apparently being attacked by a shark.
A witness watched the man, who was pulled from the water by the Maui Fire Department, and said that he seemed to be unconscious while he was pulled to shore and transferred to a gurney.
The witness also described the state of the man as he was pulled from the water and it is truly gruesome, the stuff of nightmares.
The apparent attack happened two days ago, on Saturday, May 25th at Honokowai Point near Ka'anapali Beach Park on the Hawaiian island of Maui.
According to the Maui Fire Department, the incident occurred early in the morning, around 9 AM and the man was swimming in water that was only 20 or 25 feet deep.
The conditions were clear, with light wind and only 1 foot of surf at the time. The man was apparently only 60 some-odd yards from the shore when the attack happened.
The witness, a woman named Allison Keller, was on the beach when the man was pulled from the waters by the Maui Fire Department.
She described to Fox News, in detail, the state of the man as he was being rescued. At one point, she details the fact that his skin in some places looked like it was completely torn off and his left leg from the knee down was missing. Surprisingly, there was little to no blood.
While the man's identity was not released, Keller did tell Fox News that he was vacationing on Maui with his wife. Fire Officials described the 65-year-old man's injuries as "traumatic" and "life-threatening."
Fox News reports that this is the sixth shark attack in Hawaiian waters since 2019 began, according to Hawaii's Department of Land and Natural Resources Division of Aquatic Resources.
This is the fifth fatal attack on record for Maui and experts believe it is likely the work of a tiger shark. There is a large population of tiger sharks around Maui, which has evidently contributed to the increased number of attacks, though officials don't know exactly why that is, other than potentially because of the combined conditions of shallow water and high populations of people swimming there.
For now, authorities have posted a notice warning swimmers in the area of the shark.
To read the full Fox News report, including the detailed description by witness Allison Keller, head to their article here.


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We cannot solve our problems with the same thinking we used when we created them.
Albert Einstein
The righter we do the wrong thing, the wronger we become.
Russell Ackoff
All truth passes through three stages.
First, it is ridiculed.
Second, it is violently opposed.
Third, it is accepted as being self-evident.
Arthur Schopenhauer
Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it's worth it in the end because once you get there, you can move mountains.
Steve Jobs
At the end of the day, all organizations are service providers, regardless of what they produce.
Christian Grönroos
If I have seen further it is by standing on the shoulders of Giants.
Isaac Newton (1675)
In het land der blinden is één-oog koning.
"…in a turbulent world, there's another set of cognitive skills that might matter more: the ability to rethink and unlearn."
Adam Grant, in [Think Again]
Nieuwe USM-partners die bereid zijn hun kennis te delen, en waardecreatie voor hun doelgroep centraal stellen


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GreenFuel creates new vice president position
In late February, GreenFuel Technologies Corporation announced the formation of a new position-vice president-international development-and a person to fill that position. Paul Rodzianko, GreenFuel's former board chairman, took the role, which was formed in response to increasing international interest in the company's unique carbon management process. His new duties will include developing mutually beneficial licensing relationships with a select group of partner organizations capable of executing multiple large-scale installations.