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arXiv · 1712.00254

Utilizing Domain Knowledge in End-to-End Audio Processing

Tycho Max Sylvester Tax·Jose Luis Diez Antich·Hendrik Purwins·Lars Maaløe
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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.

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BibTeXRIS

Tycho Max Sylvester Tax, Jose Luis Diez Antich, Hendrik Purwins, Lars Maaløe. 2017-12-01. Utilizing Domain Knowledge in End-to-End Audio Processing. https://arxiv.org/abs/1712.00254

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