Download Sound Source Separation: Preprocessing For Hearing Aids And Structured Audio Codin
In this paper we consider the problem of separating different sound sources in multichannel audio signals. Different approaches to the problem of Blind Source Separation (BSS), e.g. the Independent Component Analysis (ICA) originally proposed by Herault and Jutten, and extensions to this including delays, work fine for artificially mixed signals. However the quality of the separated signals is severely degraded for real sound recordings when there is reverberation. We consider the system with 2 sources and 2 sensors, and show how we can improve the quality of the separation by a simple model of the audio scene. More specifically we estimate the delays between the sensor signals, and put constraints on the deconvolution coefficients.
Download Multiresolution Sinusoidal/Stochastic Model For Voiced-Sounds
The goal of this paper is to introduce a complete analysis/resynthesis method for the stationary part of voiced-sounds. The method is based on a new class of wavelets, the Harmonic-Band Wavelets (HBWT). Wavelets have been widely employed in signal processing [1, 2]. In the context of sound processing they provided very interesting results in their first harmonic version: the Pitch Synchronous Wavelets Transform (PSWT) . We introduced the Harmonic-Band Wavelets in a previous edition of the DAFx . The HBWT, with respect to the PSWT allows one to manipulate the analysis coefficients of each harmonic independently. Furthermore one is able to group the analysis coefficients according to a finer subdivision of the spectrum of each harmonic, due to the multiresolution analysis of the wavelets. This allows one to separate the deterministic components of voiced sounds, corresponding to the harmonic peaks, from the noisy/stochastic components. A first result was the development of a parametric representation of the HBWT analysis coefficients corresponding to the stochastic components [5, 7]. In this paper we present the results concerning a parametric representation of the HBWT analysis coefficients of the deterministic components. The method recalls the sinusoidal models, where one models time-varying amplitudes and time varying phases [8, 9]. This method provides a new interesting technique for sound synthesis and sound processing, integrating a parametric representation of both the deterministic and the stochastic components of sounds. At the same time it can be seen as a tool for a parametric representation of sound and data compression.