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Perceptually constrained signal subspace method for speech enhancement - approximate solutions

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A noise reduction problem arises in many speech processing areas such as mobile devices, the speech recognition and coding systems, aids for the hearing impaired or forensic phonoscopy. An objective of speech enhancement is to improve performance of the systems in the noisy environment by reducing annoying background noise and preserving speech intelligibility. Usually one-channel applicatio[...]

Using auditory properties in multi-microphone speech enhancement


  An objective of the speech enhancement is to reduce environmental noise while preserving speech intelligibility. In a context of the multi-microphone systems the dereverberation and interference suppression are also expected. Therefore, over the past decades most efforts have been devoted to the beamforming techniques. The key idea of the beamforming is to process the microphone array signals to listen the sounds coming from only one direction. Particularly the noise reduction can be implicitly achieved by avoiding ’noisy’ directions. A linearly constrained minimum variance (LCMV) algorithm has been originally proposed by Frost [1] in the 1970 s and it is probably the most studied beamforming method since then. It minimizes a beamformer output variance subject to the set of linear equations that ensure a constant gain in a specified listening direction. A minimum variance distortionless (MVDR) method [2] can be considered as a special case of the LCVM approach. Similarly, it minimizes a beamformer output variance, but subject to a less restrictive constraint. Another popular technique is a generalized sidelobe canceler (GSC) [3, 4]. It converts the constrained optimization problem defined in the LCVM method into a more efficient, unconstrained form. In addition a processing can be split into two independent stages - the dereverberation and noise suppression, respectively. In order to work reasonably well in the reverberant environments, the classical beamforming techniques often require a system model identification i.e. knowledge of the acoustic room impulse responses or its relative ratios. These parameters can be fixed or estimated adaptively, however in general it is a difficult task. In addition the beamforming methods are usually very sensitive to the system model uncertainties. Recently, much efforts have been made to reformulate the multichannel speech enhancement problem so that the noise reduction can be [...]

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