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Erschienen in:
Quantum-Source Independent Component Analysis and Related Statistical Blind Qubit Uncoupling Methods Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

13. Monaural Speech Enhancement Based on Multi-threshold Masking

verfasst von : Masoud Geravanchizadeh, Reza Ahmadnia

Erschienen in: Blind Source Separation

Verlag: Springer Berlin Heidelberg

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Abstract

The ideal binary mask (IBM) has been assigned as a computational goal in computational auditory scene analysis (CASA) algorithms. Only time–frequency (T-F) units with local signal-to-noise ratio (SNR) exceeding a local criterion (LC) are assigned the binary value 1 in the binary mask. However, there are two problems with employing IBM in source separation applications. First, an optimum LC for a certain SNR may not be appropriate for other SNRs. Second, binary weighting may cause some parts or regions of the synthesized speech to be discarded at the output. If one employs variable weights, as opposed to the hard limiting weights (i.e., 0 or 1) taken in IBM, the above-mentioned problems can be solved considerably. In this chapter, a novel auditory-based mask, called ideal multi-threshold mask (IMM) is proposed which can be used in source separation applications. To show the potential capabilities of the new mask, a minimum mean-square error (MMSE)-based method is proposed to estimate IMM in the framework of monaural speech enhancement system. Various objective and subjective evaluation criteria show the superior performance of the new speech enhancement system as compared to a recently introduced enhancement technique.

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Vorheriges Kapitel On the Ideal Ratio Mask as the Goal of Computational Auditory Scene Analysis
Nächstes Kapitel REPET for Background/Foreground Separation in Audio
Literatur
1.
Bregman, A.S.: Auditory Scene Analysis. MIT, Cambridge (1955)
2.
Wang D.L., Brown G.J.: Computational Auditory Scene Analysis: Principles, Algorithms, and Applications. Wiley-IEEE Press, Hoboken (2006)
3.
Wang D.L.: On ideal binary mask as the computational goal of auditory scene analysis. In: P. Divenyi (ed.) Speech Separation by Humans and Machines, pp. 181–197. Kluwer Academic Publishers, Norwell (2005)
4.
Patterson R.D., Nimmo-Smith I., Holdsworth J., Rice P.: An Efficient Auditory Filterbank Based on the Gammatone Function. Report No. 2341, MRC Applied Psychology Unit, Cambridge (1985)
5.
Brungart, D., Chang, P.S., Simpson, B.D., Wang, D.L.: Isolating the energetic component of speech-on-speech masking with ideal time-frequency segregation. J. Acoust. Soc. Am. 120(6), 4007–4018 (2006)CrossRef
6.
Li, N., Loizou, P.C.: Factors influencing intelligibility of ideal binary-masked speech: implications for noise reduction. J. Acoust. Soc. Am. 123(3), 1673–1682 (2008)CrossRef
7.
Sawada H., Araki S., Makino S.: A two-stage frequency-domain blind source separation method for underdetermined convolutive mixtures. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 139–142 (2007)
8.
Mandel, M.I., Weiss, R.J., Ellis, D.P.W.: Model-based expectation-maximization source separation and localization. IEEE Trans. Audio Speech Lang. Process. 18(2), 382–394 (2010)CrossRef
9.
Alinaghi A., Wang W., Jackson P.J.B.: Spatial and coherence cues based time-frequency masking for binaural reverberant speech separation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2013)
10.
Anzalone, M.C., Calandruccio, L., Doherty, K.A., Carney, L.H.: Determination of the potential benefit of time-frequency gain manipulation. Ear Hear. 27(5), 480–492 (2006)CrossRef
11.
Cao, S., Li, L., Wu, X.: Improvement of intelligibility of ideal binary-masked noisy speech by adding background noise. J. Acoust. Soc. Am. 129(4), 2227–2236 (2011)CrossRef
12.
Fletcher, H.: Speech and Hearing in Communication. D. Van Nostrand Company, New York (1958)
13.
Dewey, G.: Relative Frequency of English Speech Sounds. Harvard University Press, Cambridge (1923)CrossRef
14.
Rothauser, E.H., Chapman, W.D., Guttman, N., Hecker, M.H.L., Nordby, K.S., Silbiger, H.R., Urbanek, G.E., Weinstock, M.: Ieee recommended practice for speech quality measurements. IEEE Trans. Audio Electroacoust. 17, 225–246 (1969)CrossRef
15.
16.
Ephraim, Y., Cohen, I.: Recent advancements in speech enhancement. In: Dorf, R.C. (ed.) The Electrical Engineering Handbook, 3rd edn. CRC Press, Boca Raton (2006)
17.
Ephraim, Y., Malah, D.: Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Trans. Acoustics Speech Signal Process. 32(6), 1109–1121 (1984)CrossRef
18.
Ephraim, Y., Malah, D.: Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Trans. Acoustics Speech Signal Process. 23(2), 443–445 (1985)CrossRef
19.
Fort, G., Lambert-Lacroix, S.: Classification using partial least squares with penalized logistic regression. Bioinformatics 21(7), 1104–1111 (2005)CrossRef
20.
Weintraub M.: A Theory and Computational Model of Auditory Monaural Sound Separation, Ph.D. Thesis, Stanford University (1985)
21.
Hu, Y., Loizou, P.C.: Evaluation of objective quality measures for speech enhancement. IEEE Trans. Audio Speech Lang. Process. 16(1), 229–238 (2008)CrossRef
22.
ITU-T.: Perceptual Evaluation of Speech Quality (PESQ): An Objective Method for End-to-End Speech Quality Assessment of Narrow-Band Telephone Networks and Speech Codecs, Series P: Telephone Transmission Quality Recommendation P.862, ITU, 1.4 (2001)
23.
ITU-R.: Recommendation BS.1534-1: Method for the Subjective Assessment of Intermediate Quality Level of Coding Systems (2001)
24.
25.
Hu K., Wang D.L.: SVM-based separation of unvoiced-voiced speech in cochannel conditions. In: IEEE International Conference on Acoustics, Speech, and Signal Processing ( ICASSP), pp. 4545–4548 (2012)
26.
Wang, Y., Han, K., Wang, D.L.: Exploring monaural features for classification-based speech segregation. IEEE Trans. Audio Speech Lang. Process. 21(2), 270–279 (2013)CrossRef
Metadaten
Titel
Monaural Speech Enhancement Based on Multi-threshold Masking
verfasst von
Masoud Geravanchizadeh
Reza Ahmadnia
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Blind Source Separation

Print ISBN: 978-3-642-55015-7

Electronic ISBN: 978-3-642-55016-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-55016-4_13

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