By Jacob Benesty, Jingdong Chen
Though noise aid and speech enhancement difficulties were studied for no less than 5 many years, advances in our figuring out and the improvement of trustworthy algorithms are extra very important than ever, as they aid the layout of adapted recommendations for basically outlined purposes. during this paintings, the authors suggest a conceptual framework that may be utilized to the numerous diversified facets of noise aid, providing a uniform method
to monaural and binaural noise relief difficulties, within the time area and within the frequency area, and related to a unmarried or a number of microphones. furthermore, the derivation of optimum filters is simplified, as are the functionality measures used for his or her evaluation.
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Extra resources for A Conceptual Framework for Noise Reduction
A same Kaiser window is applied to the output of the IFFT before the overlap-add process, again, to reduce the aliasing eﬀect caused by circular convolution. Obviously, the most critical step in the above implementation process is the computation of the noise reduction ﬁlters in the STFT subbands. 74) that we need to know the signal statistics Φin (k, n) and ρxX (k, n) or Φy (k, n) and ρxX (k, n) in order to compute the optimal noise reduction ﬁlters. In our simulations, these statistics are estimated as follows.
However, regardless of the value of L, the value of αy cannot be too large. If it is too large, the estimated statistics cannot capture the time-varying property of the nonstationary speech signals, leading to performance degradation. In general, the optimal value of αy depends on both the stationarity of the signal of interest and the noise as well as the ﬁlter length, L. It should be tuned based on the application scenario for the best noise reduction performance. Another important observation we can make from Fig.
References 1. J. Benesty and J. Chen, Optimal Time-Domain Noise Reduction Filters–A Theoretical Study. Berlin, Germany: SpringerBriefs in Electrical and Computer Engineering, 2011. 2. J. Benesty, J. Chen, Y. Huang, and I. Cohen, Noise Reduction in Speech Processing. Berlin, Germany: Springer-Verlag, 2009. 3. “DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus (TIMIT),” from the NIST TIMIT Speech Disc. 4. -F. -W. Hon, “Speaker-independent phone recognition using hidden Markov models,” IEEE Trans.