Speaker: Juan Bello, New York University
Abstract: Automatic sound source identification is a fundamental task in machine listening with a wide range of applications in environmental sound analysis including the monitoring of urban noise and bird migrations. In this talk I will discuss our efforts at addressing this problem, including data collection, annotation and the systematic exploration of a variety of methods for robust classification. I will discuss how simple feature learning approaches such as spherical k-means significantly outperform off-the-self methods based on MFCC, given large codebooks trained with every possible shift of the input representation. I will show how the size of codebooks, and the need for shifting data, can be reduced by using convolutional filters, first by means of the deep scattering spectrum, and then as part of deep convolutional neural networks. As model complexity increases, however, performance is impeded by the scarcity of labeled data, a limitation that we partially overcome with a new framework for audio data augmentation. While promising, these solutions only address simplified versions of the real-world problems we wish to tackle. At the end of the talk, I’ll discuss various steps that we’re currently undertaking to close that gap.
Bio: Juan Pablo Bello is Associate Professor of Music Technology, and Electrical & Computer Engineering, at New York University, with a courtesy appointment at NYU's Center for Data Science. In 1998 he received a BEng in Electronics from the Universidad Simón Bolívar in Caracas, Venezuela, and in 2003 he earned a doctorate in Electronic Engineering at Queen Mary, University of London. Juan's expertise is in digital signal processing, computer audition and music information retrieval, topics that he teaches and in which he has published more than 80 papers and articles in books, journals and conference proceedings. He is director of the Music and Audio Research Lab (MARL), where he leads research on music and sound informatics. His work has been supported by public and private institutions in Venezuela, the UK, and the US, including a CAREER award from the National Science Foundation and a Fulbright scholar grant for multidisciplinary studies in France. For a complete list of publications and other activities, please visit this website.