Classification of audio signals using spectrogram surfaces and extrinsic distortion measures.
EURASIP J Adv Signal Process
; 2022(1): 100, 2022.
Article
in English
| MEDLINE | ID: covidwho-2089237
ABSTRACT
Representation of one-dimensional (1D) signals as surfaces and higher-dimensional manifolds reveals geometric structures that can enhance assessment of signal similarity and classification of large sets of signals. Motivated by this observation, we propose a novel robust algorithm for extraction of geometric features, by mapping the obtained geometric objects into a reference domain. This yields a set of highly descriptive features that are instrumental in feature engineering and in analysis of 1D signals. Two examples illustrate applications of our approach to well-structured audio signals Lung sounds were chosen because of the interest in respiratory pathologies caused by the coronavirus and environmental conditions; accent detection was selected as a challenging speech analysis problem. Our approach outperformed baseline models under all measured metrics. It can be further extended by considering higher-dimensional distortion measures. We provide access to the code for those who are interested in other applications and different setups (Code https//github.com/jeremy-levy/Classification-of-audio-signals-using-spectrogram-surfaces-and-extrinsic-distortion-measures). Supplementary Information The online version contains supplementary material available at 10.1186/s13634-022-00933-9.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
EURASIP J Adv Signal Process
Year:
2022
Document Type:
Article
Affiliation country:
S13634-022-00933-9
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