TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

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

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
KustantajaIEEE
ISBN (elektroninen)9781509060146
DOI - pysyväislinkit
TilaJulkaistu - 10 lokakuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Joint Conference on Neural Networks - Rio de Janeiro, Brasilia
Kesto: 8 heinäkuuta 201813 heinäkuuta 2018

Julkaisusarja

Nimi
ISSN (elektroninen)2161-4407

Conference

ConferenceInternational Joint Conference on Neural Networks
MaaBrasilia
KaupunkiRio de Janeiro
Ajanjakso8/07/1813/07/18

Tiivistelmä

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.

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