Search

Classical music, unlike popular music, is usually recorded live with close microphone techniques. For this reason, isolated tracks are not available to create the final mixture/stream, and so the mixing process requires greater effort. Source separation methods are a potential solution to this problem. However, current algorithms are not fast enough to yield real-time separation in professional setups with dozens of microphones and sources. In this paper, we propose a fast approach consisting of a panning-based multichannel non-negative matrix factorization model to separate classical music. We tested the system on real professional recordings, where we were able to reach real-time with very low latency and promising quality. [ABSTRACT FROM AUTHOR]

In this paper, we present a multichannel nonnegative matrix factorization (MNMF) system for the task of source separation. We propose a novel signal model using spatial covariance matrices (SCM) where the mixing filter encodes the spatial information and the source variances are modeled using a NMF structure. Moreover, the proposed model is initialized with the estimated source direction of arrival (DoA) in order to mitigate the strong sensitivity to parameter initialization. The proposed system has been evaluated for the task of music source separation using a multichannel classical chamber music dataset showing that it is possible to reach real time in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques. [ABSTRACT FROM AUTHOR]