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GAFX: A General Audio Feature eXtractor

arXiv 2207.09145 ↗First posted 19 Jul 2022eess.AS cs.AI cs.SDcited by 0

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Abstract (arXiv)

Most machine learning models for audio tasks are dealing with a handcrafted feature, the spectrogram. However, it is still unknown whether the spectrogram could be replaced with deep learning based features. In this paper, we answer this question by comparing the different learnable neural networks extracting features with a successful spectrogram model and proposed a General Audio Feature eXtractor (GAFX) based on a dual U-Net (GAFX-U), ResNet (GAFX-R), and Attention (GAFX-A) modules. We design experiments to evaluate this model on the music genre classification task on the GTZAN dataset and perform a detailed ablation study of different configurations of our framework and our model GAFX-U, following the Audio Spectrogram Transformer (AST) classifier achieves competitive performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 36 references that are in the map. First posted on arXiv 19 Jul 2022.

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29 of 36 references.

References · 29

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17 more
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