A Deep Neural Network-Driven Feature Learning Method for Polyphonic Acoustic Event Detection from Real-Life Recordings

Manjunath Mulimani, Akash B. Kademani, Shashidhar G. Koolagudi

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

5 Citations (Scopus)

Abstract

In this paper, a Deep Neural Network (DNN)-driven feature learning method for polyphonic Acoustic Event Detection (AED) is proposed. The proposed DNN is a combination of different layers used to characterize multiple overlapped acoustic events in the mixture. During training, DNN is able to learn the optimal set of discriminative spectral characteristics of the overlapped (polyphonic) acoustic events. The performance of the proposed method is evaluated on the TUT Sound Event 2016 (TUT-SED 2016) real-life dataset and joint Acoustic Scene Classification (ASC) and polyphonic AED dataset. Results show that proposed approach outperforms the state-of-the-art methods.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages291-295
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - 05-2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 04-05-202008-05-2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period04-05-2008-05-20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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