Acoustic event classification using graph signals

Manjunath Mulimani; U. P. Jahnavi; Shashidhar G. Koolagudi

doi:10.1109/TENCON.2017.8228152

Acoustic event classification using graph signals

Manjunath Mulimani, U. P. Jahnavi, Shashidhar G. Koolagudi

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

In this paper, a graph signal is generated from spectrogram and features are investigated from graph signal for Acoustic Event Classification (AEC). Different acoustic events are selected from Sound Scene Database of Real Word Computing Partnership (RWCP) group. Three different noises are selected from NOISEX'92 database and added to test samples at different noise conditions separately. The recognition performance of acoustic events using proposed features and Mel-frequency cepstral coefficients (MFCCs) with clean and noisy test samples are compared. The proposed features show significantly improved recognition accuracy over MFCCs in noisy conditions.

Original language	English
Title of host publication	TENCON 2017 - 2017 IEEE Region 10 Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1812-1816
Number of pages	5
ISBN (Electronic)	9781509011339
DOIs	https://doi.org/10.1109/TENCON.2017.8228152
Publication status	Published - 19-12-2017
Event	2017 IEEE Region 10 Conference, TENCON 2017 - Penang, Malaysia Duration: 05-11-2017 → 08-11-2017

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2017-December
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2017 IEEE Region 10 Conference, TENCON 2017
Country/Territory	Malaysia
City	Penang
Period	05-11-17 → 08-11-17

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2017.8228152

Cite this

@inproceedings{35b4bc55d0dd4ac8afb0bedf9403eeaa,

title = "Acoustic event classification using graph signals",

abstract = "In this paper, a graph signal is generated from spectrogram and features are investigated from graph signal for Acoustic Event Classification (AEC). Different acoustic events are selected from Sound Scene Database of Real Word Computing Partnership (RWCP) group. Three different noises are selected from NOISEX'92 database and added to test samples at different noise conditions separately. The recognition performance of acoustic events using proposed features and Mel-frequency cepstral coefficients (MFCCs) with clean and noisy test samples are compared. The proposed features show significantly improved recognition accuracy over MFCCs in noisy conditions.",

author = "Manjunath Mulimani and Jahnavi, \{U. P.\} and Koolagudi, \{Shashidhar G.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Region 10 Conference, TENCON 2017 ; Conference date: 05-11-2017 Through 08-11-2017",

year = "2017",

month = dec,

day = "19",

doi = "10.1109/TENCON.2017.8228152",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1812--1816",

booktitle = "TENCON 2017 - 2017 IEEE Region 10 Conference",

address = "United States",

}

Mulimani, M, Jahnavi, UP & Koolagudi, SG 2017, Acoustic event classification using graph signals. in TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 1812-1816, 2017 IEEE Region 10 Conference, TENCON 2017, Penang, Malaysia, 05-11-17. https://doi.org/10.1109/TENCON.2017.8228152

Acoustic event classification using graph signals. / Mulimani, Manjunath; Jahnavi, U. P.; Koolagudi, Shashidhar G.
TENCON 2017 - 2017 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1812-1816 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2017-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Acoustic event classification using graph signals

AU - Mulimani, Manjunath

AU - Jahnavi, U. P.

AU - Koolagudi, Shashidhar G.

PY - 2017/12/19

Y1 - 2017/12/19

N2 - In this paper, a graph signal is generated from spectrogram and features are investigated from graph signal for Acoustic Event Classification (AEC). Different acoustic events are selected from Sound Scene Database of Real Word Computing Partnership (RWCP) group. Three different noises are selected from NOISEX'92 database and added to test samples at different noise conditions separately. The recognition performance of acoustic events using proposed features and Mel-frequency cepstral coefficients (MFCCs) with clean and noisy test samples are compared. The proposed features show significantly improved recognition accuracy over MFCCs in noisy conditions.

AB - In this paper, a graph signal is generated from spectrogram and features are investigated from graph signal for Acoustic Event Classification (AEC). Different acoustic events are selected from Sound Scene Database of Real Word Computing Partnership (RWCP) group. Three different noises are selected from NOISEX'92 database and added to test samples at different noise conditions separately. The recognition performance of acoustic events using proposed features and Mel-frequency cepstral coefficients (MFCCs) with clean and noisy test samples are compared. The proposed features show significantly improved recognition accuracy over MFCCs in noisy conditions.

UR - https://www.scopus.com/pages/publications/85044178688

UR - https://www.scopus.com/inward/citedby.url?scp=85044178688&partnerID=8YFLogxK

U2 - 10.1109/TENCON.2017.8228152

DO - 10.1109/TENCON.2017.8228152

M3 - Conference contribution

AN - SCOPUS:85044178688

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

SP - 1812

EP - 1816

BT - TENCON 2017 - 2017 IEEE Region 10 Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Region 10 Conference, TENCON 2017

Y2 - 5 November 2017 through 8 November 2017

ER -

Acoustic event classification using graph signals

Abstract

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All Science Journal Classification (ASJC) codes

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