Acoustic Event Classification Using Spectrogram Features

Manjunath Mulimani, Shashidhar G. Koolagudi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This paper investigates a new feature extraction method to extract different features from the spectrogram of an audio signal for Acoustic Event Classification (AEC). A new set of features is formulated and extracted from local spectrogram regions named blocks. The average recognition performance of proposed spectrogram based features and Mel-frequency cepstral coefficients (MFCCs) with their deltas and accelerations on Support Vector Machines (SVM) is compared. In this work, different categories of acoustic events are considered from the Freiburg-106 dataset. Proposed features show significantly improved performance over conventional Mel-frequency cepstral coefficients (MFCCs) for Acoustic Event Classification.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1460-1464
Number of pages5
ISBN (Electronic)9781538654576
DOIs
Publication statusPublished - 22-02-2019
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 28-10-201831-10-2018

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2018-October
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of
CityJeju
Period28-10-1831-10-18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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