Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
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TLDR
In this paper, a convolutional recurrent neural network (CRNN) was proposed for polyphonic sound event detection task and compared with CNN, RNN and other established methods, and observed a considerable improvement for four different datasets consisting of everyday sound events.Abstract:
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks CNNs are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks RNNs are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a convolutional recurrent neural network CRNN and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.read more
Citations
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