SPS AASP TC Webinar: Nonnegative Autoencoders with Applications to Music Audio Decomposing

Date: 14 May 2024
Time: 8:00 AM ET (New York Time)
Presenter(s): Dr. Meinard Müller

Abstract

Nonnegative Matrix Factorization (NMF) is a powerful technique for factorizing, decomposing, and explaining data. For example, in the field of music information retrieval, NMF has been applied for audio decomposition to decompose a music recording's magnitude spectrogram into musically meaningful spectral and activation patterns. Thanks to nonnegativity constraints in NMF and the multiplicative update rules that preserve these constraints in the training stage, it is easy to incorporate additional domain knowledge that guides the factorization to yield interpretable results. On the other hand, deep neural networks (DNNs), which can learn complex non-linear patterns in a hierarchical manner, have become omnipresent thanks to the availability of suitable hardware and software tools. However, deep learning (DL) models are often hard to interpret and control due to the massive number of trainable parameters. In this presentation, the presenter will review and discuss current research directions that combine and transfer ideas between NMF-based and DL-based learning approaches. In particular, he will show how NMF and its score-informed variants can be simulated by autoencoder-like neural network architectures in combination with projected gradient descent methods. Doing so, he aims to understand better the interaction between various regularization techniques and DL-based learning procedures while improving the interpretability of DL-based decomposition results.

 

Biography

Melissa HandaMeinard Müller received the Diploma degree in mathematics in 1997 and the Ph.D. degree in computer science in 2001 from the University of Bonn, Germany.

Since 2012, he currently holds a professorship for Semantic Audio Signal Processing at the International Audio Laboratories Erlangen, a joint institute of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and the Fraunhofer Institute for Integrated Circuits IIS. His research interests include music processing, music information retrieval, audio signal processing, and motion processing.

Dr. Müller wrote a monograph titled "Information Retrieval for Music and Motion" (Springer-Verlag, 2007) and a textbook titled "Fundamentals of Music Processing" (Springer-Verlag, 2015, www.music-processing.de). In 2020, he was elevated to IEEE Fellow for contributions to music signal processing.