Speech Enhancement Using Temporal Masking in the FFT Domain

Transcription

1 PAGE 8 Speech Enhanceent Using Teporal Masking in the FFT Doain Yao Wang, Jiong An, Teddy Surya Gunawan, and Eliathaby Abikairajah School of Electrical Engineering and Telecounications The University of New South Wales, Australia {wendy, jiongan, tsgunawan, abi}@ee.unsw.edu.au Abstract Teporal asking odels have not been previously applied in the Fast Fourier Transfor (FFT) doain for speech enhanceent applications. This paper presents a novel speech enhanceent algorith using teporal asking in the FFT doain. The proposed algorith is suitable for the cochlear speech processor and for other speech applications. The input signal is analysed using FFT and then grouped into critical bands. The noise power is estiated using a iniu statistics noise tracking algorith. A short-ter teporal asking threshold is then calculated for each critical band and a gain factor for each band is then coputed. The objective and subjective evaluations show that the teporal asking odel based speech enhanceent schee outperfors the traditional Wiener filtering approach in the FFT doain. 1. Introduction In any speech counications, the presence of background noise causes the quality and intelligibility of speech to degrade, especially when the Signal-to- Noise Ratio (SNR) is low. Speech enhanceent algoriths are of great interest because they have any applications such as speech recognition, hearing aids, and obile counications, etc. The ost popular ethod for enhancing speech in the FFT doain is Wiener filtering, which introduces speech distortion and a perceptually annoying residual noise known as usical noise, especially at low SNR. Currently, a great research effort on denoising algoriths exploiting the huan auditory hearing syste has ade considerable success, which has resulted in good quality speech with iproved intelligibility and low level usical noise (Gustafsson, Nordhol & Claesson, 1; Virag, 1999; Lin, Abikairajah & Holes, 3). All these ethods use siultaneous asking properties of the huan auditory syste, the siultaneous asking threshold is calculated using the MPEG psychoacoustic odel 1 (Black & Zeytinoglu, 1995). Most recently speech enhanceent using teporal asking and using fractional bark gaatone filters has been reported (Gunawan & Abikairajah, 4, 6a) a robust, flexible and versatile speech boosting technique has been exploited. The authors reported that the algorith has good potential for speech enhanceent applications across any types and intensities of environental noise. Teporal asking is a tie doain phenoenon and ost of the recently developed teporal asking odels operate on the output of a filter bank (Gunawan & Abikairajah, 4, 6a). To the authors knowledge, the teporal asking effect has not been previously applied in the FFT doain. This paper presents a speech enhanceent algorith using teporal asking in the FFT doain Martin s noise tracking algorith (Martin, 1) is used for noise power estiation and a perceptually odified Wiener filter (Lin et al., 3) is used for gain calculation. The teporal asking odel developed by (Gunawan & Abikairajah, 6a) is optiised to calculate the asking threshold. To evaluate the perforance of our algorith, the objective PESQ easure (ITU-T P.86) and subjective tests that adhere to ITU-T P.835 standard are utilised.. FFT Based Filter Bank Our speech processing strategy in this paper is siilar to the cochlear speech processor the incoing signal is analyzed and separated into any frequency bands. The ain spectral coponents of each critical band are then converted to stiuli of given aplitude Proceedings of the 11th Australian International Conference on Speech Science & Technology, ed. Paul Warren & Catherine I. Watson. ISBN University of Auckland, New Zealand. Deceber 6-8, 6. Copyright, Australian Speech Science & Technology Association Inc.

2 and the phases corresponding to each frequency in the critical band are discarded. The incoing audio signal was sapled at 16 khz and fraed into 18 saples with 5% overlap and with the lowest and highest frequency bins discarded. The reaining frequency bins are then divided into critical bands corresponding to the Bark scale. The FFT based filter bank, speech enhanceent and the cochlear iplant siulation are shown in Figure 1. N F is the frae size. Since the longest duration of forward asking is s, the forward asking threshold is calculated over N S successive fraes as follows: S T F N = (3) The final teporal asking threshold for each critical band TM is then chosen as the axiu of FM over N S previous fraes: TM { FM,k }, k = 1K N S = ax (4) PAGE 9 Figure 1: FFT filter bank 3. Teporal Masking in FFT Doain Teporal asking has been used successively in speech enhanceent (Gunawan & Abikairajah, 4) using a gaatone filter bank front-end. In this paper, the sae ethod is reviewed while the calculation of teporal asking threshold for the FFT filter bank is derived. Based on the forward asking experients carried out by (Jesteadt, Bacon & Lehan, 198), forward asking level FM can be well-fitted to psychoacoustic data using the following equation: ( b t)( L c) FM = a log 1 (1) FM is the aount of forward asking in db, t is the tie difference between the asker and the askee in illiseconds, L is the asker level in db, and a, b, and c, are paraeters that can be derived fro psychoacoustic data. To siplify the asking calculation, a, b, and c were set epirically to.1,.3, and, respectively. The teporal asking threshold is strongly influenced by the signals (askers) in the previous fraes. The teporal inforation is obtained by calculating the teporal distances ( T F ) between fraes. For 5% overlap between fraes,.5 N F 3 TF = 1 s () Fs Figure : Teporal asking (--- is teporal asking level for previous fraes) 4. Speech Enhanceent The objective in speech enhanceent is to suppress the noise, thus resulting in an output signal ( n) that has a higher SNR. In this section, the basic Wiener filter and the proposed Wiener filter exploiting teporal asking threshold are explained. Furtherore, the noise estiation algorith in the FFT doain for the Wiener filter approach is outlined Basic Wiener filter In this ethod, a noisy signal x ( n) is decoposed into critical band signals using FFT filter bank. The objective here is to find a Wiener gain Γ (Lin et al., 3) for each critical band. Subsequently, each noisy critical band signal is ultiplied by the denoising gain Γ to obtain the denoised critical band aplitude A = Γ X (Fig. 1). The signal can be reconstructed by using the denoised agnitude A and noisy phase φ corresponding to each frequency within the critical bands. In the case of cochlear speech processor, the phase inforation is discarded and as a result only the agnitude corresponding to centre frequency of each critical band is retained, thus providing centre Proceedings of the 11th Australian International Conference on Speech Science & Technology, ed. Paul Warren & Catherine I. Watson. ISBN University of Auckland, New Zealand. Deceber 6-8, 6. Copyright, Australian Speech Science & Technology Association Inc.

3 frequency with their respective agnitude. The following equation is used to reconstruct one frae of the signal. A M 1 fc ( n) = A sin π n + φ (5) N Fs = 1 f c is the centre frequency in each critical band, is the denoised critical band aplitude, and M is the nuber of critical bands for the particular sapling frequency Fs. In cochlear based speech processing, φ is the phase of each critical band and is set to zero. The Wiener filter gain follows (Lin et al., 3): Γ can be calculated as α vˆ Γ = (6) x ˆ vˆ is the noisy signal power, and is the estiated noise power, and α is the oversuppression factor. In order to reduce the residual usical noise, a soothing technique has been applied to the Wiener gain Γ as follows: ~ Γ ( n ) = Γ ( n ) + ( 1 γ ) Γ ( n 1) γ (7) Γ ~ is the soothed Wiener gain for the current Γ n is the Wiener gain in the current frae, frae, ( ) Γ ( n 1) is the Wiener gain in the previous frae, and γ is the soothing factor. 4.. Wiener filter incorporating teporal asking Teporal asking has been successively used for speech enhanceent (Gunawan & Abikairajah, 4, 6a). In this paper, we odified Equation (6) to incorporate the teporal asking threshold as follows: ( β TM, ) α ax vˆ Γ = (8) x ˆ is the noisy signal power, vˆ is the estiated noise power, α =. 4 is the oversuppression factor, and β =. 85 is the paraeter that controls teporal asking threshold. Note that, α and β are the paraeters that can be optiised epirically. The noise is included in Equation (8) only if it exceeds the asking threshold. Furtherore, the noise is weighted only by the aount that exceeds the asking threshold Noise estiation algorith An accurate estiate of noise power for each critical band is obtained based on the optial soothing and iniu statistics strategy by Martin (Martin, 1). Martin s algorith is based on tracking the iniu of the noisy signal power spectral density. The iniu noise statistics noise tracking ethod is based on the observation that even during speech activity a shortter power spectral density estiate the noisy signal on a frequent basis and decays to values that are representative of the noise power level. The ethod rests on the fundaental assuption that during speech pauses, or within brief periods in between words and syllables, the speech energy is close or identical to zero. Thus by tracking the iniu power within a finite window large enough to bridge high power speech segents, the noise floor can be estiated. The noise estiate is obtained by selecting the iniu value within a sliding window of 6 consecutive fraes, regardless of whether speech is present or not. Since the iniu value of a set of rando variable is saller than their ean the iniu noise estiate is usually biased. The noise estiation technique of Martin s is by far the ost accurate iplicit noise estiation algorith (Lin et al., 3). 5. Perforance Evaluation In order to assess the perforance of our proposed algorith, objective tests using PESQ (Rix, Hollier, Hekstra & Beerends, ) and subjective tests conforing to ITU-T P.835 were perfored. One speech signal sapled at 16 khz is added with three noises at three SNR levels, i.e. db, 5 db, and 1 db. The frae size was 18 saples with 5% overlap. A sentence spoken by an English feale speaker is corrupted in three background noise environents (car, white, babble, street, pink and factory noises) fro NOISEX-9 database. Two algoriths were copared, i.e. Wiener basic (Wiener) and Wiener with teporal asking (WienerTM) Objective evaluation Table 1: Objective evaluation using PESQ SNR db Wiener WienerTM Car Noise White Noise Babble Noise Street Noise Pink Noise Factory1 Noise SNR 5 db Wiener WienerTM Car Noise White Noise Babble Noise PAGE 3 Proceedings of the 11th Australian International Conference on Speech Science & Technology, ed. Paul Warren & Catherine I. Watson. ISBN University of Auckland, New Zealand. Deceber 6-8, 6. Copyright, Australian Speech Science & Technology Association Inc.

4 Street Noise Pink Noise Factory1 Noise SNR 1 db Wiener WienerTM Car Noise White Noise Babble Noise Street Noise Pink Noise Factory1 Noise Fig. 3 shows the score (MOS) for signal, background noise, and overall scale for the two ethods of speech enhanceent. The score for the noisy speech (unprocessed) files are also shown for references. Of the two ethods exained, the Wiener filtering incorporating teporal asking perfors better than the traditional Wiener filter in ters of background noise level and overall quality, while the speech quality is slightly lower. PAGE 31 The PESQ easure (ITU-T P86) was utilised for the objective evaluation. A total of 18 files fro six noises and three SNRs for each ethod were siulated. As shown in Table 1, the Wiener filtering incorporating teporal asking outperfors the basic Wiener filtering ethod in all these noises. In addition, we found that this teporal asking based Wiener filter works better in white noise environent. 5.. Subjective evaluation The subjective evaluation conforing to ITU-T P.835 standard is perfored. The standard uses separate rating scales to independently estiate the subjective quality of the speech signal alone, the background noise alone, and overall quality. The listener s uncertainty is reduced and the reliability is increased by using the above three diensions of subjective speech quality. Moreover, the previously developed toolbox, i.e. P835tool, is eployed (Gunawan & Abikairajah, 6b). A subset of the files described in the objective test was selected to reduce the length of the subjective evaluations. Only the enhanced speech corrupted by car noise at 1 db SNR was presented to the listeners. Furtherore, a high quality headphone, i.e. Sony MSRV7DJ, was utilized for the listening tests. A total of twenty subjects took part in the subjective listening test. Subjective Score Speech Background Noise Overall Noisy Speech Wiener WienerTM Figure 3: Subjective test results 5.3. Speech Spectrogra Objective easures do not give indications about the structure of the residual noise. Speech spectrogras constitute a well-suited tool for observing this structure. The speech spectrogra for SNR of 1 db is obtained by using a Hanning window of 18 saples with 5 % overlap. Fig. 4 shows the speech signals and its corresponding spectrogras. Frequency Tie x Tie(seconds) Frequency Tie(seconds) Tie Figure 4: Speech wavefor and its spectrogra The original speech signal in English: He retired quickly to his seat, he kept his back turn to the, and its spectrogra is on the half left of the figure. The half right is the processed speech and its spectrogra. We can see that our proposed algorith enhanced the noisy speech. 6. Conclusions In this paper, we have incorporated a teporal asking odel for speech enhanceent in the FFT doain. A novel way of calculating the teporal asking threshold in the FFT doain was developed. The perforance of the proposed speech enhanceent algorith was copared with the traditional Wiener filtering based speech enhanceent technique. Subjective and objective results show that the proposed algorith perfors well under various noisy conditions. This algorith can be x 1 4 Proceedings of the 11th Australian International Conference on Speech Science & Technology, ed. Paul Warren & Catherine I. Watson. ISBN University of Auckland, New Zealand. Deceber 6-8, 6. Copyright, Australian Speech Science & Technology Association Inc.

5 incorporated easily in a behind-the-ear cochlear speech processor as an alternative to the existing noise reduction algoriths. PAGE 3 7. References Black, M. & Zeytinoglu, M. (1995). Coputationally efficient wavelet packet coding of wide-band stereo audio signals. In proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Vol. 5, pp , Detroit. Gunawan, T. S. & Abikairajah, E. (4). Speech Enhanceent Using Teporal Masking and fractional bark gaatone filters. In proceedings of the 1th International Conference on Speech Science & Technology, pp.4-45, Sydney. Gunawan, T. S. & Abikairajah, E. (6a). A new forward asking odel for speech enhanceent. In proceedings of IEEE International Conference on Acoustics, Speech, and Audio Signal Processing, Vol. 1, pp , Toulouse. Gunawan, T. S. & Abikairajah, E. (6b). Subjective evaluation of speech enhanceent algoriths using ITU-T P.835 standard. In proceedings of the 1 th IEEE International Conference on Counication Systes (ICCS 6), Singapore. Gustafsson, H., Nordhol, S. E. & Claesson, I. (1). Spectral Substraction Using Reduced Delay Convolution and Adaptive Averaging, IEEE Transactions on Speech and Audio Processing, Vol. 9, Jesteadt, W., Bacon, S. P. & Lehan, J. R. (198). Forward asking as a function of frequency, asker level, and signal delay. Journal of Acoustic Society of Aerica, Vol. 71, Lin, L., Abikairajah, E. & Holes, W. H. (3). Subband noise estiation for speech enhanceent using a perceptual wiener filter, In proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, Vol. 1, pp. 8-83, Hong Kong. Martin, R. (1). Noise Power Spectral Density Estiation Based on Optial Soothing and Miniu Statistics, IEEE Transactions on Speech and Audio Processing, Vol. 9, Rix, A. W., Hollier, M. P., Hekstra, A. P. and Beerends, J. G. (). Perceptual Evaluation of Speech Quality (PESQ), the new ITU standard for end-to-end speech quality assessent, part I - tie-delay copensation. Journal of the Audio Engineering Society, 5 (1), Virag, N. (1999). Single Channel Speech Enhanceent Based on Masking Properties of the Huan Auditory Syste, IEEE Transactions on Speech and Audio Processing, Vol. 7, Proceedings of the 11th Australian International Conference on Speech Science & Technology, ed. Paul Warren & Catherine I. Watson. ISBN University of Auckland, New Zealand. Deceber 6-8, 6. Copyright, Australian Speech Science & Technology Association Inc.