NDLI: Using Deep Belief Network to Capture Temporal Information for Audio Event Classification

Content Provider	IEEE Xplore Digital Library
Author	Guo, Feng Yang, Deshun Chen, Xiaoou
Copyright Year	2015
Abstract	Audio event classification plays an important role in surveillance systems. Due to the constrain of short-time Fourier transform (STFT), the extraction of the audio frequency domain features, as the essential work among the audio event classification, still have some difficulty when conducted on a big audio frame. The traditional concatenation method of feature vector for the successive audio windows in one big audio frame is not perfect for the information redundancy in the low level audio representations. However the temporal information is very important in the audio event classification. In this paper, we try to capture the underlying temporal information in the audio event using the Deep Belief Network (DBN). Here the feature is extracted on a long time span. For the clear description, we call this kind of audio block in our method as audio unit rather than audio frame. There are mainly two contributions in this paper. First we segment the audio into units with different sizes and conduct an evaluation about the classification performance of different features on different unit sizes, including the traditional features and the DBN features. Second we present a method to merge audio features learned from multi-scale audio units to train a support vector machine (SVM) classifier. The classifier based on the merged DBN features outperforms other classifiers which are only based on the DBN features before merging or the traditional features respectively.
Starting Page	421
Ending Page	424
File Size	269742
Page Count	4
File Format	PDF
e-ISBN	9781509001880
DOI	10.1109/IIH-MSP.2015.46
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-23
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency-domain analysis Neurons Machine learning Speech recognition Feature extraction multi-scale representations Multimedia communication Deep Belief Network Mel frequency cepstral coefficient audio temporal information audio event classification
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in