Abstract / truncated to 115 words (read the full abstract)

Reducing interference noise in a noisy speech recording has been a challenging task for many years yet has a variety of applications, for example, in handsfree mobile communications, in speech recognition, and in hearing aids. Traditional single-channel noise reduction schemes, such as Wiener filtering, do not work satisfactorily in the presence of non-stationary background noise. Alternatively, supervised approaches, where the noise type is known in advance, lead to higher-quality enhanced speech signals. This dissertation proposes supervised and unsupervised single-channel noise reduction algorithms. We consider two classes of methods for this purpose: approaches based on nonnegative matrix factorization (NMF) and methods based on hidden Markov models (HMM). The contributions of this dissertation can be divided into ... toggle 10 keywords

speech enhancement – noise reduction – nonnegative matrix factorization – hidden markov model – probabilistic latent component analysis – online dictionary learning – super-gaussian distribution – mmse estimator – temporal dependencies – dynamic nmf.

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