An overview of applications and advancements in automatic sound recognition

Roneel V. Sharan*, Tom J. Moir

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

Abstract

Automatic sound recognition (ASR) has attracted increased and wide ranging interests in recent years. In this paper, we carry out a review of some important contributions in ASR techniques, mainly over the last one and a half decades. Similar to speech recognition systems, the robustness of an ASR system largely depends on the choice of feature(s) and classifier(s). We take a wider perspective in providing an overview of the features and classifiers used in ASR systems starting from early works in content-based audio classification to more recent developments in applications such as sound event recognition, audio surveillance, and environmental sound recognition. We also review techniques that have been utilized in noise robust sound recognition systems and feature optimization methods. Finally, some of the less commonly known applications of ASR are discussed.

Original languageEnglish
Pages (from-to)22-34
Number of pages13
JournalNeurocomputing
Volume200
DOIs
Publication statusPublished - 5 Aug 2016
Externally publishedYes

Keywords

  • Automatic sound recognition
  • Cepstral coefficients
  • Deep neural networks
  • Sound event recognition
  • Support vector machines
  • Time-frequency image

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