Sound Event Detection with Adaptive Frequency Selection

Zhepei Wang, Jonah Casebeer, Adam Clemmitt, Efthymios Tzinis, Paris Smaragdis

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

Abstract

In this work, we present HIDACT, a novel network architecture for adaptive computation for efficiently recognizing acoustic events. We evaluate the model on a sound event detection task where we train it to adaptively process frequency bands. The model learns to adapt to the input without requesting all frequency sub-bands provided. It can make confident predictions within fewer processing steps, hence reducing the amount of computation. Experimental results show that HIDACT has comparable performance to baseline models with more parameters and higher computational complexity. Furthermore, the model can adjust the amount of computation based on the data and computational budget.

Original languageEnglish (US)
Title of host publication2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-45
Number of pages5
ISBN (Electronic)9781665448703
DOIs
StatePublished - 2021
Event2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2021 - New Paltz, United States
Duration: Oct 17 2021Oct 20 2021

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Volume2021-October
ISSN (Print)1931-1168
ISSN (Electronic)1947-1629

Conference

Conference2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2021
Country/TerritoryUnited States
CityNew Paltz
Period10/17/2110/20/21

Keywords

  • Sound event detection
  • adaptive computation
  • convolutional recurrent neural network
  • feature selection
  • weight sharing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

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