1 introduction1 introduction 2 spectral subtraction 3 qbne 4 results 5 conclusion, & future...

1 Introduction 2 Spectral subtraction 3 QBNE 4 Results 5 Conclusion, & future work

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Enhancement of Electrolaryngeal Speech

by Reducing Leakage Noise Using Spectral Subtraction

by

Prem C. Pandey

< [email protected] >

EE Dept, IIT Bombay

Feb’07


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Abstract

Transcervical electrolarynx is a vibrator held against the neck tissue in order to provide excitation to the vocal tract, as a substitute to that provided by a natural larynx. It is of great help in verbal communication to a large number of laryngectomee patients. Its intelligibility suffers from the presence of a background noise, caused by leakage of the acoustic energy from the vibrator. Pitch synchronous application of spectral subtraction method, normally used for enhancement of speech corrupted by uncorrelated random noise, can be used for reduction of the self leakage noise for enhancement of electrolaryngeal speech. Average magnitude spectrum of leakage noise, obtained with lips closed, is subtracted from the magnitude spectrum of the noisy speech and the signal is reconstructed using the original phase spectrum. However, the spectrum of the leakage noise varies because of variation in the application pressure and movement of the throat tissue. A quantile based dynamic estimation of the magnitude spectrum without the need for silence/voice detection was found to be effective in noise reduction.

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Overview

● Introduction

● Spectral subtraction for enhancement of

electrolaryngeal speech

● Quantile-based noise estimation

● Results, summary, & ongoing work

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Natural speech productionIntroduction 1/5

Glottal excitation to vocal tract

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External electronic larynx (Barney et al 1959)

Excitation to vocal tract from external vibrator

Introduction 2/5

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Problems with artificial larynx

● Difficulty in coordinating controls

● Spectrally deficit

● Unvoiced segments substituted by voiced segments

● Background noise due to leakage of acoustic energy

Introduction 3/5

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Model of noise generation

Causes of noise generation:• Leakage of vibrations produced by vibrator membrane• Improper coupling of vibrator to neck tissue

Introduction 4/5

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Methods of noise reduction

Vibrator design Acoustic shielding of vibrator ( Epsy-Wilson et al 1996)

Piezoelectric vibrators (Katsutoshi et al 1999)

Signal processing 2-input noise cancellation based on LMS algorithm ( Epsy-Wilson et al 1996)

Single input noise cancellation ( Pandey et al 2002) based on spectral subtraction algorithm (Boll 1979 & Berouti et al 1979)

Introduction 5/5

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Spectral subtraction for enhancement of electrolayngeal speech (Pandey et al 2000)

s(n) = e(n)*hv(n), l(n) = e(n)*hl(n)x(n) = s(n) + l(n)Xn(ej) = En(ej)[Hvn

(ej) + Hln(ej)]

Assumption: hv(n) and hl (n) uncorrelated Xn(ej)2 = En(ej)2[Hvn

(ej)2 + Hln(ej)2]

Noise estimation mode: s(n) = 0Xn(ej)2 = Ln(ej)2 = En(ej)2 Hln

(ej)2

L(ej)2 : averaged over many segments

Speech enhancement mode: Yn(ej)2 = Xn(ej)2 - L(ej)2

contd…

Spect. subtrn. 1/4

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Implementation using DFT

Yn(k)2 = Xn(k)2 - L(k)2

yn(m) = IDFT [ Yn(k) ej Xn

(k)]

Modified spectral subtraction (Berouti et al 1979)

Yn(k) = Xn(k) -L(k)

Yn(k) = Yn(k) if Yn(k) L(k)

=L(k) otherwise

( : subtraction, : spectral floor, : exp. factors)

Output normalization factor for < 1 (Berouti et al 1979)

G = {(Xn(k)2 - L(k)2)/ Yn(k)2}/

Spect. subtrn. 2/4

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Spectral subtraction method with ABNE (Pandey et al 2002)

Spect. subtrn. 3/4

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Drawback of averaged noise estimation during silence

● Two modes: noise estimation & speech enhancement

● Estimated noise considered stationary over entire speech enhancement mode

● Some musical & broadband noise in the output Investigations for continuous noise estimation & signal enhancement● System with voice activity detector (Berouti et al 1979)

● Without involving speech vs non-speech detection (Stahl et al 2000, Evans et al 2002, Houwu et al 2002)

Spect. subtrn. 4/4

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Quantile-based noise estimation

Basis for the technique

● During speech segments, frequency bins tend not to be permanently occupied by speech

● Speech / non-speech boundaries detected implicitly on per frequency basis

● Noise estimates updated throughout non- speech and speech periods

QBNE 1/6

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Implementation of QBNE

● DFT of windowed speech segments

● FIFO array of past spectral values for each freq. sample is formed

● An efficient indexing algorithm used to sort the arrays to obtain particular quantile value:

– A sorted value buffer and an index buffer, for each frequency sample

– New data placed at locations of oldest data in sorted buffer by referring index buffer

– In all sorted buffers only one value needs to be placed at correct position

QBNE 2/6

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QBNE 3/4Spectral subtraction with QBNE

QBNE 3/6

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Investigations with QBNE

● Single quantile value - Quantile value which gives best visual match between quantile derived spect. & avg. spect. of noise is selected

● Two quantile values- Two quantiles for two frequency bands, which estimates noise close to avg. spect. of noise, were selected

● Frequency dependent quantile values - Estimated spectrum from noisy speech will be close match to the avg. spectrum of noise

QBNE 4/6

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QBNE 5/6Investigations with QBNE (Contd..)

● Smoothened quantile values - Matched quantiles were averaged using 9 frequency values

● SNR based dynamic quantiles - Dynamic selection of quantiles depending on signal strength

q(k) = [(q1 (k) - q0 (k)) SNR (k) / SNR1 (k)] + q0 (k) q0 (k) if q (k) < 0

q1 (k) if q (k) > q1 (k)

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Plot of SNR and frequency dependent quantilesfor three different applications of vibrator

Frequency sample

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QBNE 5/6


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Recorded and enhanced speech with (α=2,β=0.001,γ=1,N=16 ms), speaker: SP, material: /a/, /i/,and /u/ using electrolarynx Servox

Noise segment

/a/

/u/

/i/

Unprocessed Processed

Enhancement results

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Results 1/3


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Enhancement results Results 2/3

Recorded and enhanced speech with (α=2,β=0.001,γ=1, Widow length=16 ms), speaker: SP, material: question-answer pair in English “What is your name? My name is santosh” using electrolarynx Servox

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Recorded and enhanced speech with (α=2,β=0.001,γ=1), speaker: SP, material: question-answer pair in English “What is your name? My name is santosh” using electrolarynx NP-1, Servox, and Solatone

Results 3/3Enhancement results

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Conclusion 1/2

Conclusion● QBNE technique implemented for cont. updating of noise spectrum

& different methods for selection of quantile values for noise estimation investigated

● Results with QBNE during non-speech segment are comparable with results using ABNE

● Smoothened quantiles and SNR based quantiles resulted in better quality speech

● QBNE is effective for longer duration

● QBNE using SNR based dynamic quantiles is effective during long pauses

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Ongoing work

● Evaluation of intelligibility and quality improvement

● Selection of optimum quantile values for different models of electrolarynx and users

● Phase resynthesis

from magnitude spectrum

using cepstral method● Real-time implementation of noise reduction, using ADSP- BF533 board

● Analysis-synthesis for

introducing small amount of

jitter to improve naturalness

Conclusion 2/2

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References

A. Work at IIT Bombay

[1] S. M. Bhandarkar / P. C. Pandey (Supervisor), “Reduction of background noise in artificial larynx” M.Tech. Dissertation, Dept. of Electrical Engineering, IIT Bombay, January 2002.

[2] S. S. Pratapwar / P. C. Pandey (Supervisor), “Reduction of background noise in artificial larynx”, M.Tech. Dissertation, Dept. of Electrical Engineering, IIT Bombay, Feb 2004.

[3] B. R. Budiredla / P. C. Pandey (Supervisor), “Real-time implementation of spectral subtraction for enhancement of electrolaryngeal speech”, M.Tech. Dissertation, Dept. of Electrical Engineering Department, IIT Bombay, July 2005.

[4] P. Mitra / P. C. Pandey (Supervisor), “Enhancement of Electrolaryngeal Speech by Background Noise Reduction and Spectral Compensation”, M.Tech. Dissertation, Dept. of Electrical Engineering Department, IIT Bombay, July 2006.

[5] P. C. Pandey, S. M. Bhandarkar, G. K. Bachher, and P. K. Lehana, “Enhancement of alaryngeal speech using spectral subtraction” in Proc. 14th Int. Conf. Digital Signal Processing (DSP 2002), Santorini, Greece, pp. 591-594, 2002.

[6] S. S. Pratapwar, P. C. Pandey, and P. K. Lehana, “Reduction of background noise in alaryngeal speech using spectral subtraction with quantile based noise estimation”, in Proc. 7th World Conference on Systemics, Cybernetics, and Informatics,(SCI 2003 ), Orlando, Florida, 2003.

[7] P. C. Pandey, S. S. Pratapwar, and P. K. Lehana, “Enhancement of electrolaryngeal speech by reducing leakage noise using spectral subtraction with quantile based dynamic estimation of noise”, in Proc. 18th International Congress on Acoustics, (ICA2004), Kyoto, Japan, 2004.

[8] P. Mitra and P. C. Pandey, “Enhancement of electrolaryngeal speech by spectral subtraction with minimum statistics-based noise estimation” (A), J. Acoustical Society Am., 120 (5), p. 3039, November 2006

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[4] H. L. Barney, F. E. Haworth, and H. K. Dunn, "An experimental transistorized artificial larynx," Bell Systems Tech. J., vol. 38, No. 6, pp. 1337-1356, 1959.


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Speech, Signal Process., vol. ASSP-28, pp. 672-680, December 1980.[10] T. F. Quatieri, and A. V. Oppenheim, “Iterative techniques for minimum phase signal reconstruction from phase or

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[18] C. Ris and S. Dupont, “Assessing local noise level estimation methods: application to noise robust ASR,” Speech Communication, Vol. 34, No.1-2, pp.141-158, April 2001.

[19] R. Martin, “Spectral subtraction based on minimum statistic,” in Proc. 7th European Signal Processing Conf., EUSIPCO - 94, Edinburgh, Scotland, pp. 1182-1185, 13-16 September 1994.

[20] S. Rangachari, “Noise estimation algorithms for highly non-stationary environments,” M. S. Thesis, Dept. of Electrical Engineering, University of Texas at Dallas, August 2004. Available online at www.utdallas.edu/~loizou/thesis/ sundar_ms_thesis.pdf (accessed in October, 2005)

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[23] R. Martin, “Noise power spectral density estimation based on optimal smoothing and minimum statistics,” IEEE Trans. Speech and Audio Processing, Vol. 9, No. 5, pp. 504-512, July 2001.

[24] G. Doblinger, “Computationally efficient speech enhancement by spectral minima tracking in sub-bands,” in Proc 4th European Conf. Speech, Communication and Technology, EUROSPEECH'95, Madrid, Spain, pp.1513-1516, 18-21 September 1995.

[25] B. Widrow, J. R. Glover, J. M. McCool, and J. Kaunitz, “Adaptive noise canceling: principles and applications,” Proc. IEEE, vol. 63, pp. 1692-1716, December 1975.

1 introduction1 introduction 2 spectral subtraction 3 qbne 4 results 5 conclusion, & future...

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