Titlebook: Noise Reduction in Speech Processing; Israel Cohen,Yiteng Huang,Jacob Benesty Book 20091st edition Springer-Verlag Berlin Heidelberg 2009

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Jacob Benesty,Jingdong Chen,Yiteng Huang,Israel Cohenst decade. This vast subject cannot be surveyed here exhaustively, and the reader will be guided to relevant precedent reviews or key reports. Rather, special attention will be put on selected examples and their mechanistic details, aiming to demonstrate that high selectivity can be reached in these

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Introduction,e from far-away sources but they die away before reaching our ears so we do not hear them. Some others, however, reach our ears, but their energies are so weak that we do not perceive them clearly. There are sounds that we can clearly hear. Among those, some may convey very important information whi

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Mean-Squared Error Criterion,or (MSE) is, by far, the most used one because of its simplicity in terms of deriving useful filters. This chapter presents relevant error signals from which meaningful MSE criteria are built in the different domains.

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Optimal Filters in the KLE Domain,udied one, the frequency domain is not the only domain in which we can design interesting filters for noise reduction. There are other domains that may offer more advantages in the filter design. In this chapter, we consider the KLE domain. we derive two broad classes of optimal filters in this doma

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Optimal Filters in the Transform Domain,rst, different transforms can be used to replace each other without any requirement to change the algorithm formulation (optimal or suboptimal filter) and second, it is easier to fairly compare different transforms for their noise reduction performance. To do so, we need to generalize the KLE concep

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A Practical Example: Multichannel Noise Reduction for Voice Communication in Spacesuits,application to application, it is impossible (at least impossible at the present time) to find a universally feasible approach. In the design of a microphone array system and the choice of a well-suited noise reduction algorithm, there is no simple rule to follow and experiences are still crucial. I