Polyphonic Sound Event Detection Using Transposed Convolutional Recurrent Neural Network

Chandra Churh Chatterjee; Manjunath Mulimani; Shashidhar G. Koolagudi

ICASSP 2020accepted0 citations

Polyphonic Sound Event Detection Using Transposed Convolutional Recurrent Neural Network

Chandra Churh Chatterjee, Manjunath Mulimani, Shashidhar G. Koolagudi

Abstract

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.

BibTeX

@inproceedings{icassp2020_polyphonicsounde,
  title = {Polyphonic Sound Event Detection Using Transposed Convolutional Recurrent Neural Network},
  author = {Chandra Churh Chatterjee and Manjunath Mulimani and Shashidhar G. Koolagudi},
  booktitle = {ICASSP 2020},
  year = {2020}
}