Listen carefully and tell: an audio captioning system based on residual learning and gammatone audio representation

TL;DR

This paper presents an automatic audio captioning system utilizing residual learning with Gammatone audio representation, achieving improved performance over baseline systems in challenge evaluations.

Contribution

It introduces a novel audio captioning framework combining residual network encoders with attention-based decoders using Gammatone features.

Findings

Outperforms baseline systems in challenge results

Effective use of residual learning in encoder phase

Gammatone audio representation enhances captioning accuracy

Abstract

Automated audio captioning is machine listening task whose goal is to describe an audio using free text. An automated audio captioning system has to be implemented as it accepts an audio as input and outputs as textual description, that is, the caption of the signal. This task can be useful in many applications such as automatic content description or machine-to-machine interaction. In this work, an automatic audio captioning based on residual learning on the encoder phase is proposed. The encoder phase is implemented via different Residual Networks configurations. The decoder phase (create the caption) is run using recurrent layers plus attention mechanism. The audio representation chosen has been Gammatone. Results show that the framework proposed in this work surpass the baseline system in challenge results.