Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network
Área de conocimiento
Título de la revista
Biomedical Signal Processing and Control
Datos de la obra
García-Ordás, M. T., Alaiz-Moretón, H., Benítez-Andrades, J. A., García-Rodríguez, I., García-Olalla, O., & Benavides, C. (2021). Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network. Biomedical Signal Processing and Control, 69. https://doi.org/10.1016/J.BSPC.2021.102946
[EN] In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS and TESS. The results obtained were promising, outperforming the state-of–the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations or financial brokers.
- info: eu-repo/grantAgreement/Ministerio de Economía y Empresa/TSI-100909–2019-64.
Versión del editor
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