Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network
<|reference_start|>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 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.<|reference_end|>
@article{garcía-ordás2024sentiment,
    title={Sentiment analysis in non-fixed length audios using a Fully
  Convolutional Neural Network},
    author={Mar'ia Teresa Garc'ia-Ord'as, H'ector Alaiz-Moret'on, Jos'e
  Alberto Ben'itez-Andrades, Isa'ias Garc'ia-Rodr'iguez, Oscar
  Garc'ia-Olalla and Carmen Benavides},
    journal={Biomedical Signal Processing and Control, Volume 69, August 2021,
  ID 102946},
    year={2024},
    doi={10.1016/j.bspc.2021.102946},
    archivePrefix={arXiv},
    eprint={2402.02184},
    primaryClass={cs.SD cs.LG eess.AS}
}