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

Content Provider	IET Digital Library
Author	Zhang, Pengyuan Chen, Hangting Bai, Haichuan Yuan, Qingsheng
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.
Starting Page	1177
Ending Page	1183
Page Count	7
ISSN	10224653
Volume Number	28
e-ISSN	20755597
Issue Number	Issue 6, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/28/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2019.07.006
Journal	Chinese Journal of Electronics
Publisher Date	2019-11-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	2017 Show Acoustic Scene Classification Task Acoustic Scenes Acoustic Signal Processing Cepstral Analysis Commonly Used Features Computer Vision And Image Processing Technique Deep Neural Network Deep Scattering Spectrum DSS Feature Feature Extraction Image Classification Mel-frequency Cepstral Coefficient MFCC Feature Neural Computing Technique Neural Nets Probability Theory Signal Classification Speech And Audio Signal Processing Speech Processing Technique Speech Recognition Speech Recognition And Synthesis State-of-the-art Time Delay Neural Network Framework Statistics Stochastic Linearised SCUC
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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