Utilizing Domain Knowledge in End-to-End Audio Processing
Sound
2017-12-04 v1 Audio and Speech Processing
Machine Learning
Abstract
End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations. In this paper we present preliminary work that shows the feasibility of training the first layers of a deep convolutional neural network (CNN) model to learn the commonly-used log-scaled mel-spectrogram transformation. Secondly, we demonstrate that upon initializing the first layers of an end-to-end CNN classifier with the learned transformation, convergence and performance on the ESC-50 environmental sound classification dataset are similar to a CNN-based model trained on the highly pre-processed log-scaled mel-spectrogram features.
Cite
@article{arxiv.1712.00254,
title = {Utilizing Domain Knowledge in End-to-End Audio Processing},
author = {Tycho Max Sylvester Tax and Jose Luis Diez Antich and Hendrik Purwins and Lars Maaløe},
journal= {arXiv preprint arXiv:1712.00254},
year = {2017}
}
Comments
Accepted at the ML4Audio workshop at the NIPS 2017