POLYPHONIC SOUND EVENT DETECTION USING TRANSPOSED CONVOLUTIONAL RECURRENT NEURAL NETWORK

被引：0
|
作者：
Chatterjee, Chandra Churh [1 ]
Mulimani, Manjunath [2 ]
Koolagudi, Shashidhar G. [3 ]
机构：
[1] Jalpaiguri Govt Engn Coll, Dept CSE, Jalpaiguri 735102, India
[2] Manipal Acad Higher Educ, Manipal Inst Technol, Dept CSE, Manipal 576104, India
[3] Natl Inst Technol Karnataka, Dept CSE, Surathkal 575025, India
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年
关键词：
Sound Event Detection (SED); Deep Neural Networks (DNN); Convolution Neural Networks (CNN); Recurrent Neural Networks (RNN); Transposed CNN (TCNN); Instantaneous Frequency spectrogram (IFgram);
D O I：
10.1109/icassp40776.2020.9054628
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In 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.
引用
收藏
页码：661 / 665
页数：5
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