Speech Enhancement, Human Auditory system, Digital hearing aid, Noise maskers, Tone maskers, Signal to Noise ratio, Mean Square Error

Transcription

1 Volume 119 No , ISSN: (on-line version) url: SUBBAND APPROACH OF SPEECH ENHANCEMENT USING THE PROPERTIES OF HUMAN AUDITORY SYSTEM FOR DIGITAL HEARING AID 1 Dr. D. Deepa, 2 Dr. C. Poongodi 1 Professor, Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India 2 Professor, Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India 1 ddeepa@bitsathy.ac.in, 2 poongodic@bitsathy.ac.in Abstract: Enhancement of noisy speech is an important preprocessing step in speech processing algorithms. The proposed work is to enhance the speech signal by eliminating the noise present in the speech by using the properties of human auditory system. This work is focused for the application of the speech enhancement algorithm in Digital hearing aids. In addition to the removal of noise the quality of the speech is also maintained in the proposed method. When the signal is enhanced in the hearing aids the background noise also gets amplified this creates listener fatigue condition for the hearing impaired people. Considering this drawback of the conventional hearing aids an algorithm is proposed by the use of the properties of human auditory system. From the results of the proposed method it is identified that considerable amount of background noise is reduced and Signal to noise Ratio is improved and the Mean square error is reduced. Key words Speech Enhancement, Human Auditory system, Digital hearing aid, Noise maskers, Tone maskers, Signal to Noise ratio, Mean Square Error 2259

2 Introduction Speech Enhancement algorithms can be categorized based on the number of channels available. Single channel and Multi channel namely. In multichannel techniques reference signals will be present for processing the degraded speech signal. Where as in single channel techniques the enhancement must be done using only the noisy speech signal without any reference, this is a challenging job due to the unavailability of information on noise or clean signal. In this work, single channel algorithm is proposed for speech enhancement in Digital hearing aid (Udayashankara, V. and Shivaprasad, A.P, 2006) using subband approach. In the subband approach the input speech signal is separated as subbands based on the frequency and processed separately. The advantage of using subband approach is that, Intelligibility of speech will not be suffered much and the noise removal will also be in considerable amount. In real time speech signal will not be degraded by noise uniformly at all frequencies. To identify the noise affected portions of speech effectively subband approach can be used. In the input noisy speech signal, high frequency noise must be eliminated and in addition to that noise present in low frequency range also to be identified and reduced. This is a challenging work in single channel environment. Frequency domain Input Noisy Speech Signal Perceptual Gain calculation g P Estimate of Speech Enhanced signal Gain calculation (g) using priori SNR and Noisy speech spectrum Priori SNR calculation Fig 1: Flow diagram of the proposed work. 2260

3 The flow diagram of the proposed work is given in the figure 1. Initially the input noisy speech is converted into frequency domain signal using Fourier transform. The transformed signal is processed in two steps. In the first step perceptual gain is calculated based on the human auditory system (Asmaa Amehraye, Dominique Pastor, Ahmed Tamtaoui and Driss Aboutajdine 2009, Baer, T., Moore, B.C. and Gatechouse, S 1993). This values are identified from the human auditory threshold, tonal maskers and noise maskers. First phase enhancement is done through the perceptual gain. From that priori SNR is calculated and further second level of gain calculation id done to estimate the enhanced speech signal. Proposed Speech Enhancement Module Calculation of Gain for noise reduction In the conventional speech enhancement model (Cohen 2004, Cyril Plapous, Claude Marro, Laurent Mauuary and Pascal Scalart 2004), the input noisy speech signal is represented as y(n) = s(n)+z(n), where s(n) and z(n) denote the speech and the noise signal, respectively in the same way S(n,w), Z(n,w) and Y(n,w) designate the spectral component of the short time frame of the speech s(n), the noise z(n) and the noisy speech y(n) respectively. Reduction of noise components in the input noisy speech signal is through the calculations of perceptual gain g P (n,w) and adaptive noise estimation algorithms (Sundarrajan Rangachari, Philipos C. Loizou and Yi Hu, 2004). In the proposed method the calculation of the perceptual gain is performed through the hearing threshold calculation and tonal and noise maskers calculation. Using the above two masking thresholds the global noise masking threshold is identified. Calculation of perceptual gain The quality of the speech signal is improved in two steps. First the perceptual gain is calculated using the properties of human auditory system.( Balamurugan.E, jagadeesan.a, 2018)From the gain the priori SNR is identified and further the second level of gain calculation using the above calculated priori SNR (Deepa, D. and Shanmugam, A. 2011) is done. The properties of human auditory system are of Absolute threshold of hearing and masking (Joachim Thiemann 2001) the frequencies based on human perceptual quality (Hornsby, B.W. and 2261

4 SPL (db) Ricketts, T.A2001). This is done by identifying the portions of a signal that are perceptible and not noticeable to normal human ear. Absolute threshold of hearing 150 Absolute Threshold of Hearing (Hz scale) Frequency (Hz) The experimental data of absolute threshold of hearing (Deepa, D. and Shanmugam, A 20, 2011) is modeled by the equation given below, where f is frequency in Hertz: T q f 3.64 * f / e 0.6 f * f 4 (1) The audio signal that falls below this threshold cannot be heard by human, so it can be eliminated from our calculations. Spectral Analysis The spectral components of the input noisy speech signal is estimated using the given equation P k PN log n N 1 0 Y n w n e 2 j kn N 2 0 k N 2 (2) 2262

5 PN is the power normalization constant and it is fixed as 35dB by experimental calculations. w(n) is the hanning window used to find the spectrum of the signal. Identifications of Maskers Tonal and noise maskers are identified from the power spectral components. Based on the bark distance tonal components are separated and the spectral peaks are identified. From these values Tonal maskers PTM(k) is computed. S T P(k) for P(k) P(k) P(k P(k 1) k ) 7 db (3) Where k [2],2 k [3],63 k [6],127 k 63;( ;( ;( KHz) 11 KHz) 20 KHz) P TM k log j P k j db (4) Similarly noise maskers are identified from the remaining spectral peaks that are not included as tone. P NM k log j 0.1P j db (5) These tonal and noise maskers will be scrutinized based on the absolute hearing threshold. If the maskers fall below the threshold then it will be removed. Calculation of Thresholds for tone and noise and Global Masking threshold 2263

6 From the tonal and noise maskers, individual tone and noise masking thresholds are computed. Tonal masker thresholds and noise masker thresholds are given by T T TM NM i, j i, j P P TM NM j j 0.275z j 0.175z j SF i, j SF i, j (6) SF is the spreading function used to specify the minimum level of nearby frequencies that can be detectable by human ear. Using absolute hearing threshold, tonal and noise masking thresholds global masking threshold is calculated by adding the individual threshold values. T i, j log 0.1T M m 1 q i 0.1T L l 1 NM i,j 0.1T TM i,j dbspl (7) This threshold helps to find the perceptual gain in the first phase of noise reduction process. g p (i, j) 1 max 1 N(i, j) 2 T(i, j) 1,0 (8) Where N is the estimated noise signal. The percpetual gain calculated from equation (8) is used for estimating first level of enhanced speech signal and is given in equation (9). ~ S n,w g p. Y n,w (9) 2264

7 The Perceptual gain is used to find the priori probability using the formula given below. The apriori SNR (Samy Elshamy, Nilesh Madhu, Wouter Tirry, Tim Fingscheidt, 2015) (Scalart, S. and Vieira Filho, J. 1996) which is an important parameter for noise suppression, is expressed in the below equation. ˆ pri (n,w) ~ E( S(n, w) E( Nˆ (n,w) 2 2 ) ) The noise power spectrum Nˆ (n,w) is estimated using the adaptive noise estimation ~ algorithm, the estimate of S(n, w) of the speech spectrum is obtained from the perceptual gain calculation obtained in the first phase of the proposed work. () g(n, w) 1 ˆ pri ˆ (n,w) pri (n,w) (11) Equation (11) specifies the gain calculation using priori SNR values. This gian is used to identify the enhanced speech using the equation (12) Ŝ(m, w) g(n,w)(y(n,w) (12) The enhanced speech signal given in the above equation is the product of gain factor obtained by two step process and the noisy speech spectrum. Results and Discussion The proposed method is evaluated based on the subjective and objective measure and the results are given below (Yi Hu and Philipos C. Loizou 2008). The Signal to Noise ratio values and mean square values are identified and compared with convention methods like decision directed approach and two step decision directed approach (Richard C. Hendriks, Richard 2265

8 Heusdens and Jesper Jensen 2005), (Joon-Hyuk Chang and Yun-Sik Park 2007). Further the time domain and frequency domain plots are given in figure 2 and in figure 3 respectively. Fig 2: Time domain plots For Colored factory 5 db signal (a) Clean signal, (b) Noisy signal, (c) Signal enhanced using two step decision directed approach, (d) Signal enhanced using proposed method. 2266

9 SNR in db Fig 2: Spectrogram plots for Colored factory 5 db signal (a) Clean signal, (b) Noisy signal, (c) Signal enhanced using two step decision directed approach, (d) Signal enhanced using proposed method Comparison of SNR values Noisy speech Samples DD Approach TSDD Approach Proposed Method Fig 4: Comparison of SNR values 2267

10 Comparison of SNR values is shown in Figure 4. The SNR value is improved for the proposed approach. The proposed method is compared with the conventional decision directed and two step decision directed approach. From the results, it is shown that proposed method provides maximum SNR. Table 1: Comparison of Mean Square Error values Test samples in the following noisy condition Babble Train Restaurant Station Airport Car Exhibition Input Mean Square Error SNR in db DD Approach TSDD Approach Proposed method

11 MSE values of the proposed single channel speech enhancement algorithms are given in Table 1. The proposed methods MSE value is minimum compared to conventional decision directed approach and two step decision directed approach and the error values are in the range of to Conclusion In this work, single channel speech enhancement algorithms using subband approach is proposed to enhance noisy speech signal. This proposed algorithm makes use of the perceptual feature of the human ear for identifying the thresholds like noise masking threshold and tone masking thresholds. The spectral components of a speech signal decides the value of tone and noise masking thresholds which are used to adapt the perceptual gain factor. Experimental results shows that the proposed approach removes more amounts of residual noise, but also guarantee speech quality. This method of speech enhancement using perceptual gain factor is an improved version with better quality and intelligibility that can be implemented for enhancing signals in Digital hearing aids. References 1. Udayashankara, V. and Shivaprasad, A.P. Digital Hearing Aid - A Review, World congress on Medical Physics and Biomedical Engineering, Brejil, Aug pp , Asmaa Amehraye, Dominique Pastor, Ahmed Tamtaoui and Driss Aboutajdine From maskee to audible noise in perceptual speech enhancement, International Journal of Signal Processing, Vol. 5, No. 2, pp , Baer, T., Moore, B.C. and Gatechouse, S. Spectral contrast enhancement of speech in noise for listeners with sensorineural hearing impairment: effects on intelligibility, quality, and response times, Journal on Rehabilitation Research and Development, Vol. 30, No. 1, pp , Cohen, I. On the Decision Directed Approach of Ephraim and Malah, Proceedings of IEEE International Conference, ICASSP 2004, pp ,

12 5. Cyril Plapous, Claude Marro, Laurent Mauuary and Pascal Scalart A two-step noise reduction technique, Proceedings of IEEE International Conference, ICASSP 2004, pp , Sundarrajan Rangachari, Philipos C. Loizou and Yi Hu A Noise Estimation Algorithm with Rapid Adaptation for Highly Non-Stationary Environments, Proceedings of IEEE International Conference, ICASSP 2004, I , Deepa, D. and Shanmugam, A. Speech Enhancement Algorithm Using Sub band Two Step Decision Directed Approach with Adaptive Weighting factor and Noise Masking Threshold, Journal of Computer Science, Vol. 7, No. 6, pp , Joachim Thiemann Acoustic Noise Suppression for Speech Signals using Auditory Masking effects, Thesis, McGill University Montreal, Canada, July Hornsby, B.W. and Ricketts, T.A. The effects of compression ratio, signal-to- noise ratio and level on speech recognition in normal-hearing listeners, Journal of the Acoustical Society of America, Vol. 9, No. 6, pp , Deepa, D. and Shanmugam, A. Analysis of Sub band Speech Enhancement Technique for Digital Hearing Aids, International Journal of Computer and Network Security (IJCNS), Vol. 2, No. 5, pp , Deepa, D. and Shanmugam, A. Enhancement of Noisy speech signal based on Variance and Modified Gain function with PDE preprocessing Technique for Digital Hearing Aids, Journal of Scientific and Industrial Research (JSIR), Vol. 70, pp , Samy Elshamy, Nilesh Madhu, Wouter Tirry, Tim Fingscheidt, An Iterative Speech Model-Based a priori SNR Estimator, INTERSPEECH Conference, September Scalart, S. and Vieira Filho, J. Speech enhancement based on a priori signal to noise estimation, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Atlanta, GA, Vol. 2, pp , Yi Hu and Philipos C. Loizou Evaluation of Objective Quality Measures for Speech Enhancement, IEEE Transactions on Audio, Speech and Language Processing, Vol. 16,No1, pp ,

13 15. Richard C. Hendriks, Richard Heusdens and Jesper Jensen Improved Decision Directed Approach for Speech Enhancement using an Adaptive Time Segmentation, Interspeech 2005, Lisbon, Portugal, Balamurugan.E, jagadeesan.a, Geographic Routing Resilient To Location Errors, International Journal Of Innovations In Scientific And Engineering Research, Vol 5 No3,21-26, Mar (2018). 17. Joon-Hyuk Chang and Yun-Sik Park A Novel Approach to a robust a priori SNR estimator in Speech Enhancement, IEICE Transactions on Communication, Vol. E90-B, No. 8, pp ,

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