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Multichannel source separation has been a popular topic, and recently proposed methods based on the local Gaussian model have provided promising results despite its high computational cost when several sensors are used. This drawback limits the practical application of this approach to tasks such as sound field reconstruction or virtual reality. In this study, we presented a numerical approach to reduce the complexity of multichannel nonnegative matrix factorization to address the task of audio source separation for scenarios with a large number of sensors, such as high-order ambisonics encoding. In particular, we proposed a parallel driver to compute the multiplicative update rules in MNMF approaches. It is designed to function on both sequential and multicore computers, as well as graphics processing units (GPUs). The solution attempts to reduce the computational cost of multiplicative update rules by using Cholesky decomposition and solving several triangular equation systems. The driver was evaluated for different scenarios, with promising results in terms of execution times for both the CPU and GPU. To the best of our knowledge, this proposal is the first to address the problem of reducing the computational cost of full-rank MNMF-based systems using parallel and high-performance techniques. [ABSTRACT FROM AUTHOR]

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 harmonic constrained Multichannel Non-Negative Matrix Factorization (MNMF) method for the task of blind music source separation. In this model, the mixing filter encodes the spatial information in terms of magnitude and phase differences between channels whereas the source variances are modelled using a harmonic constrained NMF structure. In this work, the spatial covariance matrix is obtained from the constant-Q transform to account for the frequency logarithmic scale inherent in music signals and reduce the dimensionality of the parameters. Moreover, to mitigate the strong sensitivity to parameter initialization, we propose to initialize the spatial weights with the output of the steered response power (SRP) with the phase transform (PHAT) algorithm. The proposed method has been evaluated for the task of music source separation using a multichannel classical chamber music dataset with several polyphony and reverberation setups. Furthermore, a comparison with other state-of-the-art signal decomposition methods has been accomplished showing reliable results in terms of BSS_EVAL metrics.

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]