Deep neural network bottleneck features for acoustic event recognition

Seongkyu Mun; Suwon Shon; Wooil Kim; Hanseok Ko

doi:10.21437/Interspeech.2016-1112

Deep neural network bottleneck features for acoustic event recognition

Seongkyu Mun, Suwon Shon, Wooil Kim, Hanseok Ko

Research output: Contribution to journal › Conference article › peer-review

17 Citations (Scopus)

Abstract

Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to conventional features (MFCC, Mel-spectrum, etc.), this paper employs rhythm, timbre, and spectrum-statistics features for effectively extracting acoustic characteristics from audio signals. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is verified by comparing to conventional methods.

Original language	English
Pages (from-to)	2954-2957
Number of pages	4
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume	08-12-September-2016
DOIs	https://doi.org/10.21437/Interspeech.2016-1112
Publication status	Published - 2016
Event	17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States Duration: 2016 Sept 8 → 2016 Sept 16

Bibliographical note

Publisher Copyright:
Copyright © 2016 ISCA.

Keywords

Acoustic event recognition
Bottleneck feature
Deep belief network
Deep neural network
Feature extraction

ASJC Scopus subject areas

Language and Linguistics
Human-Computer Interaction
Signal Processing
Software
Modelling and Simulation

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10.21437/Interspeech.2016-1112

Cite this

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title = "Deep neural network bottleneck features for acoustic event recognition",

abstract = "Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to conventional features (MFCC, Mel-spectrum, etc.), this paper employs rhythm, timbre, and spectrum-statistics features for effectively extracting acoustic characteristics from audio signals. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is verified by comparing to conventional methods.",

keywords = "Acoustic event recognition, Bottleneck feature, Deep belief network, Deep neural network, Feature extraction",

author = "Seongkyu Mun and Suwon Shon and Wooil Kim and Hanseok Ko",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 ISCA.; 17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 ; Conference date: 08-09-2016 Through 16-09-2016",

year = "2016",

doi = "10.21437/Interspeech.2016-1112",

language = "English",

volume = "08-12-September-2016",

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journal = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

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TY - JOUR

T1 - Deep neural network bottleneck features for acoustic event recognition

AU - Mun, Seongkyu

AU - Shon, Suwon

AU - Kim, Wooil

AU - Ko, Hanseok

PY - 2016

Y1 - 2016

N2 - Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to conventional features (MFCC, Mel-spectrum, etc.), this paper employs rhythm, timbre, and spectrum-statistics features for effectively extracting acoustic characteristics from audio signals. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is verified by comparing to conventional methods.

AB - Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to conventional features (MFCC, Mel-spectrum, etc.), this paper employs rhythm, timbre, and spectrum-statistics features for effectively extracting acoustic characteristics from audio signals. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is verified by comparing to conventional methods.

KW - Acoustic event recognition

KW - Bottleneck feature

KW - Deep belief network

KW - Deep neural network

KW - Feature extraction

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U2 - 10.21437/Interspeech.2016-1112

DO - 10.21437/Interspeech.2016-1112

M3 - Conference article

AN - SCOPUS:84994212686

SN - 2308-457X

VL - 08-12-September-2016

SP - 2954

EP - 2957

JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

T2 - 17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016

Y2 - 8 September 2016 through 16 September 2016

ER -

Deep neural network bottleneck features for acoustic event recognition

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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