Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network
Sound
2024-02-08 v1 Machine Learning
Audio and Speech Processing
Abstract
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.
Cite
@article{arxiv.2402.02184,
title = {Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network},
author = {María Teresa García-Ordás and Héctor Alaiz-Moretón and José Alberto Benítez-Andrades and Isaías García-Rodríguez and Oscar García-Olalla and Carmen Benavides},
journal= {arXiv preprint arXiv:2402.02184},
year = {2024}
}