An empirical study of weakly supervised audio tagging embeddings for general audio representations
Abstract
We study the usability of pre-trained weakly supervised audio tagging (AT) models as feature extractors for general audio representations. We mainly analyze the feasibility of transferring those embeddings to other tasks within the speech and sound domains. Specifically, we benchmark weakly supervised pre-trained models (MobileNetV2 and EfficientNet-B0) against modern self-supervised learning methods (BYOL-A) as feature extractors. Fourteen downstream tasks are used for evaluation ranging from music instrument classification to language classification. Our results indicate that AT pre-trained models are an excellent transfer learning choice for music, event, and emotion recognition tasks. Further, finetuning AT models can also benefit speech-related tasks such as keyword spotting and intent classification.
Cite
@article{arxiv.2209.15167,
title = {An empirical study of weakly supervised audio tagging embeddings for general audio representations},
author = {Heinrich Dinkel and Zhiyong Yan and Yongqing Wang and Junbo Zhang and Yujun Wang},
journal= {arXiv preprint arXiv:2209.15167},
year = {2022}
}
Comments
Odyssey 2022