Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/15004
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dc.contributor.authorChatterjee C.C.
dc.contributor.authorMulimani M.
dc.contributor.authorKoolagudi S.G.
dc.date.accessioned2021-05-05T10:16:11Z-
dc.date.available2021-05-05T10:16:11Z-
dc.date.issued2020
dc.identifier.citationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings , Vol. 2020-May , , p. 661 - 665en_US
dc.identifier.urihttps://doi.org/10.1109/ICASSP40776.2020.9054628
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15004-
dc.description.abstractIn this paper we propose a Transposed Convolutional Recurrent Neural Network (TCRNN) architecture for polyphonic sound event recognition. Transposed convolution layer, which caries out a regular convolution operation but reverts the spatial transformation and it is combined with a bidirectional Recurrent Neural Network (RNN) to get TCRNN. Instead of the traditional mel spectrogram features, the proposed methodology incorporates mel-IFgram (Instantaneous Frequency spectrogram) features. The performance of the proposed approach is evaluated on sound events of publicly available TUT-SED 2016 and Joint sound scene and polyphonic sound event recognition datasets. Results show that the proposed approach outperforms state-of-the-art methods. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.en_US
dc.titlePolyphonic sound event detection using transposed convolutional recurrent neural networken_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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