In this paper is presented the DESAM Toolbox, a set of Matlab functions dedicated to the estimation of widely used spectral models for music signals. Although those models can be used in Music Information Retrieval (MIR) tasks, the core functions of the toolbox do not focus on any specific application. It is rather aimed at providing a range of state-of-the-art signal processing tools that decompose music files according to different signal models, giving rise to different “mid-level” representations. After motivating the need for such a toolbox, this paper offers an overview of the overall organization of the toolbox, and describes all available functionalities.

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In this article we investigate how the local Gaussian model (LGM) can be applied to separate sound sources in the higher-order ambisonics (HOA) domain. First, we show that in the HOA domain, the mathematical formalism of the local Gaussian model remains the same as in the microphone domain. Second, using an off-the shelf source separation toolbox (FASST) based on the local Gaussian model, we validate the efficiency of the approach in the HOA domain by comparing the performance of toolbox in the HOA domain with its performance in the microphone domain. To do this we discuss and run some simulations to ensure a fair comparison. Third, we check the efficiency of the local Gaussian model compared to other available source separation techniques in the HOA domain. Simulation results show that separating sources in the HOA domain results in a 1 to 12 dB increase in signal-to-distortion ratio, compared to the microphone domain. Multichannel source separation, local Gaussian model, Wiener filtering, 3D audio, Higher Order Ambisonics (HOA).

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