Data-driven audio recognition: a supervised dictionary approach
Sound
2021-01-01 v1 Audio and Speech Processing
Abstract
Machine hearing is an emerging area. Motivated by the need of a principled framework across domain applications for machine listening, we propose a generic and data-driven representation learning approach. For this sake, a novel and efficient supervised dictionary learning method is presented. Experiments are performed on both computational auditory scene (East Anglia and Rouen) and synthetic music chord recognition datasets. Obtained results show that our method is capable to reach state-of-the-art hand-crafted features for both applications
Cite
@article{arxiv.2012.14761,
title = {Data-driven audio recognition: a supervised dictionary approach},
author = {Imad Rida},
journal= {arXiv preprint arXiv:2012.14761},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1812.04748