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.


Mohammadiha, Nasser
KTH Royal Institute of Technology
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Sept. 16, 2013

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