Our 2017 TASLP journal paper “Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection” has received the IEEE Signal Processing Society's Best Paper Award 2023. I am deeply honoured to have contributed to such impactful work in our field. Special congratulations go to Emre Çakır and Giambattista Parascandolo on their outstanding work!
Emre Cakir, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen, and Tuomas Virtanen. Convolutional recurrent neural networks for polyphonic sound event detection. Transactions on Audio, Speech and Language Processing: Special issue on Sound Scene and Event Analysis, 25(6):1291–1303, June 2017. doi:10.1109/TASLP.2017.2690575.
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Abstract
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.