03883nam a22005895i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137041000800172050001300180050001600193050001500209072001700224072001600241072002300257072002300280082001600303100002900319245012400348260006100472264006100533300004200594336002600636337002600662338003600688347002400724490005800748505029000806520167501096650001702771650002202788650002202810650003002832650001702862650004102879650002202920650002702942650002702969700002902996700003603025710003403061773002003095776003603115830005803151856004803209912001403257942000703271999001503278978-3-642-23250-3DE-He21320141014113555.0cr nn 008mamaa110914s2012 gw | s |||| 0|eng d a97836422325039978-3-642-23250-37 a10.1007/978-3-642-23250-32doi aeng 4aTK5102.9 4aTA1637-1638 4aTK7882.S65 7aTTBM2bicssc 7aUYS2bicssc 7aTEC0080002bisacsh 7aCOM0730002bisacsh04a621.3822231 aBenesty, Jacob.eauthor.10aSpeech Enhancement in the STFT Domainh[electronic resource] /cby Jacob Benesty, Jingdong Chen, Emanuël A.P. Habets. 1aBerlin, Heidelberg :bSpringer Berlin Heidelberg,c2012. 1aBerlin, Heidelberg :bSpringer Berlin Heidelberg,c2012. aVII, 109p. 5 illus.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aSpringerBriefs in Electrical and Computer Engineering0 aIntroduction -- Single-Channel Speech Enhancement with a Gain -- Single-Channel Speech Enhancement with a Filter -- Multichannel Speech Enhancement with Gains -- Multichannel Speech Enhancement with Filters -- The Bifrequency Spectrum in Speech Enhancement -- Summary and Perspectives. aThis work addresses this problem in the short-time Fourier transform (STFT) domain. We divide the general problem into five basic categories depending on the number of microphones being used and whether the interframe or interband correlation is considered. The first category deals with the single-channel problem where STFT coefficients at different frames and frequency bands are assumed to be independent. In this case, the noise reduction filter in each frequency band is basically a real gain. Since a gain does not improve the signal-to-noise ratio (SNR) for any given subband and frame, the noise reduction is basically achieved by liftering the subbands and frames that are less noisy while weighing down on those that are more noisy. The second category also concerns the single-channel problem. The difference is that now the interframe correlation is taken into account and a filter is applied in each subband instead of just a gain. The advantage of using the interframe correlation is that we can improve not only the long-time fullband SNR, but the frame-wise subband SNR as well. The third and fourth classes discuss the problem of multichannel noise reduction in the STFT domain with and without interframe correlation, respectively. In the last category, we consider the interband correlation in the design of the noise reduction filters. We illustrate the basic principle for the single-channel case as an example, while this concept can be generalized to other scenarios. In all categories, we propose different optimization cost functions from which we derive the optimal filters and we also define the performance measures that help analyzing them. 0aEngineering. 0aComputer science. 0aFourier analysis. 0aAcoustics in engineering.14aEngineering.24aSignal, Image and Speech Processing.24aFourier Analysis.24aEngineering Acoustics.24aModels and Principles.1 aChen, Jingdong.eauthor.1 aHabets, Emanuël A.P.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9783642232497 0aSpringerBriefs in Electrical and Computer Engineering40uhttp://dx.doi.org/10.1007/978-3-642-23250-3 aZDB-2-ENG cEB c4276d4276