ESC: Dataset for Environmental Sound Classification - paper replication data

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Abstract:

One of the obstacles in research activities concentrating on environmental sound classification is the scarcity of suitable and publicly available datasets. This paper tries to address that issue by presenting a new annotated collection of 2 000 short clips comprising 50 classes of various common sound events, and an abundant unified compilation of 250 000 unlabeled auditory excerpts extracted from recordings available through the Freesound project. The paper also provides an evaluation of human accuracy in classifying environmental sounds and compares it to the performance of selected baseline classifiers using features derived from mel-frequency cepstral coefficients and zero-crossing rate.

Dataset downloads:

• Whole dataset: dx.doi.org/10.7910/DVN/YDEPUT
• ESC-50 @ GitHub
• ESC-10 @ GitHub

Paper:

• Author version of the paper: ESC: Dataset for Environmental Sound Classification.

Citing:

K. J. Piczak. ESC: Dataset for Environmental Sound Classification. In Proceedings of the 23rd ACM international conference on Multimedia, pp. 1015-1018, ACM, 2015.

[DOI: http://dx.doi.org/10.1145/2733373.2806390]

Supplementary materials:

• Exploratory analysis of the dataset / source code (Jupyter/IPython notebook)
• Poster:

Related work:

• Environmental Sound Classification with Convolutional Neural Networks