Learning from Between-class Examples for Deep Sound Recognition
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.
 The propose data augmentation and BC learning is relevant, much robust than frequency jitter or simple data augmentation. In equation 2, please check the measure of the mixture. Why not simply use a dB criteria ? The comments about applying a CNN to local features or novel approach to increase sound recognition could be completed with some ICLR 2017 work towards injected priors using Chirplet Transform. The authors might discuss more how to extend their model to image recognition, or at least of other modalities as suggested. Section 3.2.2 shall be placed later on, and clarified. Discussion on mixing more than two sounds leads could be completed by associative properties, we think... ?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning from Between-class Examples for Deep Sound Recognition
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The propose data augmentation and BC learning is relevant, much robust than frequency jitter or simple data augmentation. In equation 2, please check the measure of the mixture. Why not simply use a dB criteria ? The comments about applying a CNN to local features or novel approach to increase sound recognition could be completed with some ICLR 2017 work towards injected priors using Chirplet Transform. The authors might discuss more how to extend their model to image recognition, or at least of other modalities as suggested. Section 3.2.2 shall be placed later on, and clarified. Discussion on mixing more than two sounds leads could be completed by associative properties, we think... ?
assistant
 The propose data augmentation and BC learning is relevant, much robust than frequency jitter or simple data augmentation. In equation 2, please check the measure of the mixture. Why not simply use a dB criteria ? The comments about applying a CNN to local features or novel approach to increase sound recognition could be completed with some ICLR 2017 work towards injected priors using Chirplet Transform. The authors might discuss more how to extend their model to image recognition, or at least of other modalities as suggested. Section 3.2.2 shall be placed later on, and clarified. Discussion on mixing more than two sounds leads could be completed by associative properties, we think... ?