@inproceedings{1f84f282dc6e4905aa5e3293f814cca1,
title = "Feature learning with deep scattering for urban sound analysis",
abstract = "In this paper we evaluate the scattering transform as an alternative signal representation to the mel-spectrogram in the context of unsupervised feature learning for urban sound classification. We show that we can obtain comparable (or better) performance using the scattering transform whilst reducing both the amount of training data required for feature learning and the size of the learned codebook by an order of magnitude. In both cases the improvement is attributed to the local phase invariance of the representation. We also observe improved classification of sources in the background of the auditory scene, a result that provides further support for the importance of temporal modulation in sound segregation.",
keywords = "Unsupervised learning, acoustic event classification, machine learning, scattering transform, urban",
author = "Justin Salamon and Bello, {Juan Pablo}",
year = "2015",
month = dec,
day = "22",
doi = "10.1109/EUSIPCO.2015.7362478",
language = "English (US)",
series = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "724--728",
booktitle = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
note = "23rd European Signal Processing Conference, EUSIPCO 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
}