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 Carmen Benavides
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.