A comparison of Deep Learning methods for environmental sound detection

Juncheng Li; Wei Dai; Florian Metze; Shuhui Qu; Samarjit Das

ICASSP 2017accepted0 citations

A comparison of Deep Learning methods for environmental sound detection

Juncheng Li, Wei Dai, Florian Metze, Shuhui Qu, Samarjit Das

Abstract

Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically available. This work thus presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest path, library, train, etc. In total, 13 hours of stereo audio recordings are available, making this one of the largest datasets available.

BibTeX

@inproceedings{icassp2017_acomparisonofdee,
  title = {A comparison of Deep Learning methods for environmental sound detection},
  author = {Juncheng Li and Wei Dai and Florian Metze and Shuhui Qu and Samarjit Das},
  booktitle = {ICASSP 2017},
  year = {2017}
}