Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

ZHANG Pengyuan; CHEN Hangting; BAI Haichuan; YUAN Qingsheng

doi:10.1049/cje.2019.07.006

Volume 28 Issue 6

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ZHANG Pengyuan, CHEN Hangting, BAI Haichuan, et al., “Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1177-1183, 2019, doi: 10.1049/cje.2019.07.006

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ZHANG Pengyuan, CHEN Hangting, BAI Haichuan, et al., “Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1177-1183, 2019, doi: 10.1049/cje.2019.07.006

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Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

doi: 10.1049/cje.2019.07.006

1.
Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;
2.
University of Chinese Academy of Sciences, Beijing 100049, China;
3.
National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China

Funds: This work is supported by the National Natural Science Foundation of China (No.11590774, No.11590770), the Key Science and Technology Project of the Xinjiang Uygur Autonomous Region (No.2016A03007-1), and the Pre-research Project for Equipment of General Information System (No.JZX2017-0994/Y306).

Received Date: 2018-01-15
Rev Recd Date: 2018-08-02
Publish Date: 2019-11-10

Abstract

Abstract

As one of the most commonly used features, Mel-frequency cepstral coefficients (MFCCs) are less discriminative at high frequency. A novel technique, known as Deep scattering spectrum (DSS), addresses this issue and looks to preserve greater details. DSS feature has shown promise both on classification and recognition tasks. In this paper, we extend the use of DSS feature for acoustic scene classification task. Results on Detection and classification of acoustic scenes and events (DCASE) 2016 and 2017 show that DSS provided 4.8% and 17.4% relative improvements in accuracy over MFCC features, within a state-of-the-art time delay neural network framework.

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References(12)

References

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