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

María Teresa García-Ordás, Héctor Alaiz-Moretón, José Alberto Benítez-Andrades, Isaías García-Rodríguez, Oscar García-Olalla +1 more
2/3/2024
cs.SDcs.LGeess.AS

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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.

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Cite this paper

@article{garcaords2024sentiment,
  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},
  year   = {2024},
  eprint = {2402.02184},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.02184v1}
}

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