SW-WAVENET: Learning Representation from Spectrogram and Wavegram Using Wavenet for Anomalous Sound Detection

Chen, Haihui; Ran, Lusen; Sun, Xun; Cai, Chengtao

doi:10.1109/icassp49357.2023.10096742

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…The various plots of these formants depict the different peaks at the various frequencies. Spectrogram of a voice signals [24] is yet another voice feature. They are a kind of a waveform comprising of various events which change as the time goes by.…”

Section: Introductionmentioning

confidence: 99%

A Novel Voice Feature AVA and its Application to the Pathological Voice Detection Through Machine Learning

Altaf,

Mahdin,

Maskat

et al. 2023

IJACSA

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Voice pathology is a universal problem which must be addressed. Traditionally, this malady is treated by using the surgical instruments in the varied healthcare settings. In the current era, machine learning experts have paid an increasing attention towards the solution of this problem by exploiting the signal processing of the voice. For this purpose, numerous voice features have been capitalized to classify the healthy and pathological voice signals. In particular, Mel-Frequency Cepstral Coefficients (MFCC) is a widely used feature in speech and audio signal processing. It denotes spectral characteristics of a voice signal, particularly of human speech. The modus operandi of MFCC is too time-consuming, which goes against the hasty and urgent nature of the modern times. This study has developed a yet another voice feature by utilizing the average value of the amplitudes (AVA) of the voice signals. Moreover, Gaussian Naive Bayes classifier has been employed to classify the given voice signals as healthy or pathological. Apart from that, the dataset has been acquired from the SVD (Saarbrucken Voice Database) to demonstrate the workability of the proposed voice feature and its usage in the classifier. The machine experimentation rendered very promising results. Particularly, Recall, F1 and accuracy scores obtained, are 100%, 83% and 80%, respectively. These results vividly imply that the proposed classifier can be installed in various healthcare settings.

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Section: Introductionmentioning

confidence: 99%