TY - JOUR
AU1 - Bu, Zhaoyang
AU2 - Zhang, Hanhaodi
AU3 - Zhu, Xiaohu
AB - Abstract: 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.
TI - GAFX: A General Audio Feature eXtractor
JF - Computing Research Repository
DO - 10.48550/arxiv.2207.09145
DA - 2022-07-19
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/gafx-a-general-audio-feature-extractor-ROP7Ru0poJ
VL - 2023
IS - 2207
DP - DeepDyve
ER -