Trends in audio scene source counting and analysis

Audio scene analysis involves a variety of tasks to obtain information from an audio environment. Audio source counting is one such task that has implications to many other aspects of audio analysis, yet it is relatively unexplored. This work presents the first review of the audio source counting literature and aims to convey the significance of this task to the wider domain of audio analysis. We identify and discuss connections between audio source counting and other more commonly studied audio analysis tasks. In addition, a review of the publicly available audio datasets is presented, highlighting the lack of datasets geared towards audio source counting. Our goal of this review paper is to promote future research of audio source counting. • Audio source counting determines the number of sound events in an audio recording. • Research shows audio source counting can help reduce errors in audio analysis. • A lack of audio source counting datasets and data standardization impedes research.

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Publication Details

Journal
Machine Learning with Applications
Published
2024-10-19
DOI
https://doi.org/10.1016/j.mlwa.2024.100593
Citations
1
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.35
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article

Trends in audio scene source counting and analysis

Michael Nigro, Sridhar Krishnan
1 citations
Machine Learning with Applications
Music and Audio Processing
0.35
article

Trends in audio scene source counting and analysis

Michael Nigro, Sridhar Krishnan
article en
1 citations

Abstract

Audio scene analysis involves a variety of tasks to obtain information from an audio environment. Audio source counting is one such task that has implications to many other aspects of audio analysis, yet it is relatively unexplored. This work presents the first review of the audio source counting literature and aims to convey the significance of this task to the wider domain of audio analysis. We identify and discuss connections between audio source counting and other more commonly studied audio analysis tasks. In addition, a review of the publicly available audio datasets is presented, highlighting the lack of datasets geared towards audio source counting. Our goal of this review paper is to promote future research of audio source counting. • Audio source counting determines the number of sound events in an audio recording. • Research shows audio source counting can help reduce errors in audio analysis. • A lack of audio source counting datasets and data standardization impedes research.

Machine Learning with ApplicationsVol. 18
University of Toronto (CA)
Openalex Percentile: Top 43%
Music and Audio Processing
0.35
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