Accelerating Non-Negative Matrix Factorization for Audio Source Separation on Multi-Core and Many-Core Architectures


Non-negative matrix factorization (NMF) has been successfully used in audio source separation and parts-based analysis; however, iterative NMF algorithms are computationally intensive, and therefore, time to convergence is very slow on typical personal computers. In this paper, we describe high performance parallel implementations of NMF developed using OpenMP for shared-memory multicore systems and CUDA for many-core graphics processors. For 20 seconds of audio, we decrease running time from 18.5 seconds to 2.6 seconds using OpenMP and 0.6 seconds using CUDA. These performance increases allow source separation to be carried out on entire songs in a number of seconds, a process which was previously impractical with respect to time. We give insight into how such significant speed gains were made and encourage the development and use of parallel music information retrieval software.

International Society for Music Information Retrieval Conference