The paper “Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification,” has won the 2020 Institute of Electrical and Electronics Engineers (IEEE) Signal Processing Society (SPS) Signal Processing Letters Best Paper Award. The article, by Justin Salamon, who at the time it was published was a senior research scientist at the NYU Tandon School of Engineering’s Center for Urban Science and Progress (CUSP) and the Music and Audio Research Lab (MARL) at NYU Steinhardt, and Juan Pablo Bello, the director of both CUSP and MARL, appeared in the March 2017 issue of the journal. It was honored for its “exceptional merit and broad interest on a subject related to the Society’s technical scope.” (To be eligible for consideration, an article must have appeared in Signal Processing Letters within a five-year window.)
As described in their award-winning paper, Bello and Salamon proposed the idea of employing deep convolutional neural networks (CNNs) – a class of neural networks originally developed for computer vision tasks – to learn discriminative spectro-temporal patterns in environmental sounds. They were among the first researchers ever to explore this application, and they found that although the high-capacity models were well suited to the task, the relative scarcity of labeled data impeded the process.
The best-paper award will be presented at the IEEE Conference on Acoustics, Speech, & Signal Processing (ICASSP), which is scheduled to be held in Toronto on June 6-11, 2021.