Title: Designing and Learning Features for Music Information Retrieval
Abstract:
This talk discusses a mix of concepts, problems and techniques at the crossroads of signal processing, machine learning and music. I will start by motivating the use of content-based methods for the analysis and retrieval of music. Then, I will introduce recent work done at my lab on a variety of music information retrieval (MIR) problems such as automatic chord recognition, music structure analysis, cover song identification and instrument recognition. In the process of doing so, I’ll review the impact of feature design for specific MIR tasks, suggest that existing feature extraction methods in audio can be re-conceptualized as deep, multi-layer and trainable systems combining affine transforms and subsampling operations, and show a few examples where deep learning matches or outperforms the current state of the art in music and sound classification.
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 60 papers and articles in books, journals and conference proceedings. In 2008, he co-founded the Music and Audio Research Lab (MARL), where he leads research on music 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: https://files.nyu.edu/jb2843/public/
Abstract:
This talk discusses a mix of concepts, problems and techniques at the crossroads of signal processing, machine learning and music. I will start by motivating the use of content-based methods for the analysis and retrieval of music. Then, I will introduce recent work done at my lab on a variety of music information retrieval (MIR) problems such as automatic chord recognition, music structure analysis, cover song identification and instrument recognition. In the process of doing so, I’ll review the impact of feature design for specific MIR tasks, suggest that existing feature extraction methods in audio can be re-conceptualized as deep, multi-layer and trainable systems combining affine transforms and subsampling operations, and show a few examples where deep learning matches or outperforms the current state of the art in music and sound classification.
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 60 papers and articles in books, journals and conference proceedings. In 2008, he co-founded the Music and Audio Research Lab (MARL), where he leads research on music 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: https://files.nyu.edu/jb2843/public/
