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.

Automated summary

The study introduces a sentiment analysis method that can process audio of any length, using Mel spectrogram and Mel Frequency Cepstral Coefficients as audio description methods, and a Fully Convolutional Neural Network architecture as classifier. The results, validated on three well-known datasets, show promising performance surpassing existing methods, and the method's ability to analyze sentiment in near real time is particularly useful for a wide range of fields such as call centers, medical consultations, and financial brokers.