Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network

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...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: García-Ordás, María Teresa, Alaiz-Moretón, Héctor, Benítez-Andrades, José Alberto, García-Rodríguez, Isaías, García-Olalla, Oscar, Benavides, Carmen
Format: Article
Language:English
Subjects:
Artificial neural networks
Data mining
Sentiment analysis
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2402.02184