Title: A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification,
Abstract: In the past, Acoustic Scene Classification systems have been based on hand
crafting audio features that are input to a classifier. Nowadays, the common
trend is to adopt data driven techniques, e.g., deep learning, where audio
representations are learned from data. In this paper, we propose a system that
consists of a simple fusion of two methods of the aforementioned types: a deep
learning approach where log-scaled mel-spectrograms are input to a
convolutional neural network, and a feature engineering approach, where a
collection of hand-crafted features is input to a gradient boosting machine. We
first show that both methods provide complementary information to some extent.
Then, we use a simple late fusion strategy to combine both methods. We report
classification accuracy of each method individually and the combined system on
the TUT Acoustic Scenes 2017 dataset. The proposed fused system outperforms
each of the individual methods and attains a classification accuracy of 72.8%
on the evaluation set, improving the baseline system by 11.8%.