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

arXiv: https://arxiv.org/abs/2402.02184 · HTML: https://mlsanity.com/paper/2402.02184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 60 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis](https://mlsanity.com/paper/2006.00492/) (2020)
- [Retinal vessel segmentation based on Fully Convolutional Neural Networks](https://mlsanity.com/paper/1812.07110/) (2018)
- [Human Vocal Sentiment Analysis](https://mlsanity.com/paper/1905.08632/) (2019)

## Built on by (most cited first, 1 of 1)

- [Beyond Simple Fusion: Adaptive Gated Fusion for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2510.01677/) (2025)

## Abstract (arXiv)

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.