Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
This work addresses the problem of detecting overlapping sound events in noisy environments, which is incremental as it builds on known CNN and RNN approaches.
The authors tackled polyphonic sound event detection by combining convolutional and recurrent neural networks into a CRNN, achieving considerable improvements over existing methods on four datasets of everyday sounds.
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) 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.