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Acoustic scene classification using teacher-student learning with soft-labels

2019-04-23 03:42:20
Hee-Soo Heo, Jee-weon Jung, Hye-jin Shim, Ha-Jin Yu

Abstract

Acoustic scene classification identifies an input segment into one of the pre-defined classes using spectral information. The spectral information of acoustic scenes may not be mutually exclusive due to common acoustic properties across different classes, such as babble noises included in both airports and shopping malls. However, conventional training procedure based on one-hot labels does not consider the similarities between different acoustic scenes. We exploit teacher-student learning with the purpose to derive soft-labels that consider common acoustic properties among different acoustic scenes. In teacher-student learning, the teacher network produces soft-labels, based on which the student network is trained. We investigate various methods to extract soft-labels that better represent similarities across different scenes. Such attempts include extracting soft-labels from multiple audio segments that are defined as an identical acoustic scene. Experimental results demonstrate the potential of our approach, showing a classification accuracy of 77.36 % on the DCASE 2018 task 1 validation set.

Abstract (translated)

声学场景分类使用光谱信息将输入段识别为一个预先定义的类。声学场景的光谱信息可能不是相互排斥的,因为不同类别的声学特性相同,例如机场和购物中心都包含了嘈杂的噪音。然而,传统的基于热标签的训练程序并没有考虑不同声学场景之间的相似性。我们利用师生学习的目的,推导出考虑不同声学场景中常见声学特性的软标签。在师生学习中,教师网络产生软标签,以此为基础对学生网络进行培训。我们研究了各种方法来提取软标签,更好地表示不同场景之间的相似性。这些尝试包括从定义为相同声学场景的多个音频片段中提取软标签。实验结果证明了我们的方法的潜力,在DCAS2018任务1验证集上显示分类精度为77.36%。

URL

https://arxiv.org/abs/1904.10135

PDF

https://arxiv.org/pdf/1904.10135.pdf


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