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--- |
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license: cc-by-4.0 |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- audio-classification |
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pretty_name: VocalSimilarity |
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dataset_info: |
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features: |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: label |
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dtype: string |
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- name: speaker |
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dtype: string |
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- name: subset |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 21758664803.439 |
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num_examples: 269523 |
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download_size: 0 |
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dataset_size: 21758664803.439 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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### Dataset Description |
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This multi-species dataset was customized to benchmark k-NN retrieval and cluster separation tecniques on Human and Songbird vocalizations. |
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## Download Dataset |
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```python |
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from huggingface_hub import snapshot_download |
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snapshot_download('anonymous-submission000/vocsim', local_dir = "data/vocsim", repo_type="dataset" ) |
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``` |
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For more usage details, please refer to the GitHub repository: https://anonymous.4open.science/anonymize/neural_embeddings-6EE5 |
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### Data Fields |
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1. **Subset**: Specifies the subset/category of the dataset. It can indicate whether the sample is from humans or songbirds, and possibly more detailed categorization. |
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2. **Audio**: Contains the audio sample. |
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3. **Label**: Represents the label or class of the audio clip, indicating the type of vocalization or sound. |
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4. **Speaker**: Identifies the speaker or source of the vocalization in the case of human datasets, or the individual bird in the case of songbird datasets. |
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### Human Datasets |
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1. [**AMI**](https://groups.inf.ed.ac.uk/ami/corpus/): The AMI Meeting Corpus comprises 100 hours of multi-modal meeting recordings, including audio data for utterances, words, and vocal sounds, alongside detailed speaker metadata. |
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2. [**TIMIT**](https://catalog.ldc.upenn.edu/LDC93S1): The TIMIT dataset contains manual phonetic transcriptions of utterances read by 630 English speakers with various dialects. |
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3. [**VocImSet**](https://zenodo.org/records/1340763): The Vocal Imitation Set contains recordings of 236 unique sound sources being imitated by 248 speakers. |
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4. [**VocalSketch**](https://zenodo.org/records/1251982): The Vocal Sketch Dataset contains two sets of 10'705 and 5'700 imitations respectively of 240 sounds. |
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### Songbird Datasets |
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1. [**DAS**](https://elifesciences.org/articles/68837): The Deep Audio Segmenter Dataset features single male Bengalese finch songs, including 473 vocalizations of 6 vocalization types. |
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2. [**Tomka**](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/655689/2023.09.04.555475v1.full.pdf): The Gold-Standard Zebrafinch dataset contains 48,059 vocalizations of 36 vocalization types from 4 zebra finches. |
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3. [**Nicholson**](https://figshare.com/articles/dataset/Bengalese_Finch_song_repository/4805749/9): The Bengalese finch song repository includes songs of four Bengalese finches recorded in the Sober lab at Emory University and manually clustered by two authors. |
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4. [**Elie**](https://figshare.com/articles/dataset/Vocal_repertoires_from_adult_and_chick_male_and_female_zebra_finches_Taeniopygia_guttata_/11905533/1): Vocal repertoires from zebra finches, collected between 2011 and 2014 at the University of California Berkeley by Julie E Elie. This dataset contains 3,500 vocalizations from 50 individuals and 65 vocalization types. |
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## Contact |
