Learning from Between-class Examples for Deep Sound RecognitionDownload PDF

15 Feb 2018 (modified: 21 Apr 2024)ICLR 2018 Conference Blind SubmissionReaders: Everyone
Abstract: Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds. We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio. We then input the mixed sound to the model and train the model to output the mixing ratio. The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher’s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes. The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial. Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning. As a result, we achieved a performance surpasses the human level.
TL;DR: We propose an novel learning method for deep sound recognition named BC learning.
Keywords: sound recognition, supervised learning, feature learning
Code: [![github](/images/github_icon.svg) mil-tokyo/bc_learning_sound](https://github.com/mil-tokyo/bc_learning_sound) + [![Papers with Code](/images/pwc_icon.svg) 4 community implementations](https://paperswithcode.com/paper/?openreview=B1Gi6LeRZ)
Data: [ESC-50](https://paperswithcode.com/dataset/esc-50), [UrbanSound8K](https://paperswithcode.com/dataset/urbansound8k-1)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 5 code implementations](https://www.catalyzex.com/paper/arxiv:1711.10282/code)
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