Automatic Classification of Volumes of Water Using Swallow Sounds from Cervical Auscultation

Subramani, S and Achuth Rao, MV and Giridhar, D and Hegde, PS and Kumar Ghosh, P (2020) Automatic Classification of Volumes of Water Using Swallow Sounds from Cervical Auscultation. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 4 - 8 May 2020, Barcelona, pp. 1185-1189.

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Abstract

The signatures of swallowing vary depending on the volume of bolus swallowed. Among existing instrumental methods, cervical auscultation (CA) captures the acoustic signatures of the swallow sound. Although many features present in the literature can characterize volumes of swallow using CA, they require manual annotations of the different components in the sound. In this work, a rich set of acoustic features, the ComParE 2016 acoustic feature set (OS) is used to investigate whether several temporal, spectral, vocal and source features and their functionals provide cues for volume classification. Experiments are performed with CA data from 56 subjects, with dry swallow and swallows of 2ml, 5ml, and 10ml of water. Three types of classification namely, dry-vs-2ml, dry-vs-5ml and dry-vs-10ml are performed separately to analyze characteristic features. Experiments reveal that OS, which does not require annotations, performs better than the baseline features that require annotation. Within OS, the features unrelated to voice source yield a better performance than the features related to voice source. In this subset of features, MFCC, RASTA filtered audio spectrum and RMS energy are found to be consistently the top performing features across all three types of classifications. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Speech communication, Acoustic features; Acoustic signature; Audio spectrum; Automatic classification; Instrumental methods; Manual annotation; Source features; Volume classifications, Audio signal processing
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	06 Feb 2023 09:24
Last Modified:	06 Feb 2023 09:24
URI:	https://eprints.iisc.ac.in/id/eprint/79909

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