Utilizing Domain Knowledge in End-to-End Audio Processing

Autor:	Tax, Tycho Max Sylvester, Antich, Jose Luis Diez, Purwins, Hendrik, Maaløe, Lars
Rok vydání:	2017
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at the ML4Audio workshop at the NIPS 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.00254 Zobrazit plný text záznamu View this record from Arxiv