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Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features

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

Details

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherIEEE
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 10 Oct 2018
Publication typeA4 Article in a conference publication
EventInternational Joint Conference on Neural Networks - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

Name
ISSN (Electronic)2161-4407

Conference

ConferenceInternational Joint Conference on Neural Networks
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Abstract

In this paper, we propose a stacked convolutional and recurrent neural network (CRNN) with a 3D convolutional neural network (CNN) in the first layer for the multichannel sound event detection (SED) task. The 3D CNN enables the network to simultaneously learn the inter-and intra-channel features from the input multichannel audio. In order to evaluate the proposed method, multichannel audio datasets with different number of overlapping sound sources are synthesized. Each of this dataset has a four-channel first-order Ambisonic, binaural, and single-channel versions, on which the performance of SED using the proposed method are compared to study the potential of SED using multichannel audio. A similar study is also done with the binaural and single-channel versions of the real-life recording TUT-SED 2017 development dataset. The proposed method learns to recognize overlapping sound events from multichannel features faster and performs better SED with a fewer number of training epochs. The results show that on using multichannel Ambisonic audio in place of single-channel audio we improve the overall F-score by 7.5%, overall error rate by 10% and recognize 15.6% more sound events in time frames with four overlapping sound sources.

ASJC Scopus subject areas

Publication forum classification

Field of science, Statistics Finland