Tampere University of Technology

TUTCRIS Research Portal

A convolutional neural network approach for acoustic scene classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017
Number of pages8
ISBN (Electronic)9781509061815
Publication statusPublished - 30 Jun 2017
Publication typeA4 Article in a conference publication
EventInternational Joint Conference on Neural Networks -
Duration: 1 Jan 1900 → …

Publication series

ISSN (Electronic)2161-4407


ConferenceInternational Joint Conference on Neural Networks
Period1/01/00 → …


This paper presents a novel application of convolutional neural networks (CNNs) for the task of acoustic scene classification (ASC). We here propose the use of a CNN trained to classify short sequences of audio, represented by their log-mel spectrogram. We also introduce a training method that can be used under particular circumstances in order to make full use of small datasets. The proposed system is tested and evaluated on three different ASC datasets and compared to other state-of-the-art systems which competed in the 'Detection and Classification of Acoustic Scenes and Events' (DCASE) challenges held in 20161 and 2013. The best accuracy scores obtained by our system on the DCASE 2016 datasets are 79.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9% improvements with respect to the baseline system. Finally, when tested on the DCASE 2013 evaluation dataset, the proposed system manages to reach a 77.0% accuracy, improving by 1% the challenge winner's score.

ASJC Scopus subject areas

Publication forum classification

Field of science, Statistics Finland

Downloads statistics

No data available