International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.11, No.7, Jul. 2019
Wiener Filter Based Noise Reduction Algorithm with Perceptual Post Filtering for Hearing Aids
Full Text (PDF, 959KB), PP.69-81
This paper presents a filter bank summation method to perform spectral splitting of input signal for binaural dichotic presentation along with dynamic range compression coupled with noise reduction algorithm based on wiener filter. This helps to compensate the effect of spectral masking, reduced dynamic range, and improves speech perception for moderate sensorineural hearing loss in the adverse listening conditions. We have considered cascaded structure of noise reduction technique; Filter Bank Summation (FBS) based amplitude compression and spectral splitting. Wiener filter produces the enhanced signal by removing unwanted noise. The signal is split into eighteen frequency bands, ranging from 0-5KHz, based on auditory critical bandwidths. To reduce the dynamic range, amplitude compression is carried out using constant compression factor in each of the bands. Subjective and objective assessment based on Mean Opinion Score (MOS) and Perceptual Evaluation of Speech Quality (PESQ) scores, respectively, are used to test the Perceived quality of speech for different Signal-to-Noise Ratio (SNR) conditions. Vowel Consonant Vowel (VCV) syllable /aba/ and sentences were used as the test material. The results of the listening tests showed MOS scores for processed speech sentence “sky that morning was clear and bright blue” (4.41, 4.2, 3.96, 3.6, 3.08 and 2.66) as compared with unprocessed speech MOS scores ( 4.53, 1.21, 1.16, 1.06, 0.8, 0.483) for SNR values of ∞, +6, +3, 0, -3 and -6 dB respectively, and PESQ values (Left Channel: 2.6192, 2.5355, 2.5646, 2.5513, 2.5221, and 2.4309; Right Channel: 2.5889, 2.3001, 2.3714, 2.4710, 2.3636, and 2.4712) for SNR values of ∞, +6, +3, 0, -3 and -6 dB respectively, indicating the improvement in the perceived quality for different SNR conditions. To evaluate the intelligibility of the perceived speech, listening test was carried out for hearing impaired (moderate Sensorineural Hearing Loss (SNHL)) persons in the presence of background noise using Modified Rhyme Test (MRT).The test material consists 50 sets of monosyllabic words of consonant-vowel-consonant (CVC) form with six words in each set. Each subject responded for a total of 1800 presentations (300 words x 6 different SNR conditions). Results of the listening tests (using MRT) showed maximum improvement of (27.299%, 23.95%, 24.503%, 23.602%, and 23.498%) in the speech recognition scores at SNR values of (-6dB, -3dB, 0dB, +3dB, +6dB) compared to unprocessed speech recognition scores. Reductions in response times compared to unprocessed speech response times at lower SNR values were observed. The decrease in response times at the SNR values of -6, -3, 0, +3 and+6 dB were 1.581, 1.41, 1.329, 1.279, and 1.01s, respectively, indicating improvement in intelligibility of the speech at lower SNR values.
Cite This Paper
Rajani S. Pujar, Pandurangarao N. Kulkarni, "Wiener Filter Based Noise Reduction Algorithm with Perceptual Post Filtering for Hearing Aids", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.7, pp. 69-81, 2019.DOI: 10.5815/ijigsp.2019.07.06
Sunitha, S. L., & Udayashankara, V. (2011). A Practical approach: Adaptive filtering for sensorineural hearing impairment. International Journal of Advanced Biotechnology and Research, 2(1), 183-189.
Yang, L., & Philipos C. L. (2008). A geometric approach to spectral subtraction, Speech Communication, 50(1), 453-466.
Cornelis, B., Moonen, M., & Wouters, J., (2011). Performance analysis of multichannel Wiener filter based noise reduction in hearing aids under second order statistics estimation errors. IEEE Transactions on Audio, Speech, and Language Processing, 19 (5), 1368–1381.
Zhang, M., & Er. M. H. (1995). Adaptive beamforming by microphone array. IEEE Global Telecommunications Conference, 1, 163-167.
Nathaniel, A., Whitma1 & Janet C., R. (1994). Noise reduction algorithms for digital hearing aids. IEEE 16th Annual International Conference on Engineering Advances: New Opportunities for Biomedical Engineers, 1294-1295.
Sid, P. B. (2006). Auditory compression and hearing loss. Acoustics Today, 2 (2), 30.
Jingdong C., Jacob B., Yiteng H., & Simon D. (2006). New insights into the noise reduction wiener filter. IEEE Transactions on Audio, Speech, and Language Processing, 14(4).
Luts, H., Maj, J. B., Soede, W., & Wouters, J. (2004). Better speech perception in noise with an assistive multimicrophone array for hearing aids. Ear Hearing, 25(1), 411–420.
Cornelis, B., Moonen, M., & Wouters, J. (2010). Binaural cue preservation in binaural hearing aids with reduced-bandwidth multichannel Wiener filter based noise reduction. International Workshop on Acoustic Echo and Noise Control.
Chatlani, N., Fischer, E., & Soraghan, J. (2010). Spatial noise reduction in binaural hearing aids. European Signal Processing Conference.
Cornelis, B., Moonen, M., & Wouters, J. (2011). A VAD-robust multichannel wiener filter algorithm for noise reduction in hearing aids. IEEE International Conference on Acoustics, Speech, and Signal Processing, 281–284.
Cornelis, B., Moonen, M., & Wouters, J. (2009). Comparison of frequency domain noise reduction strategies based on multichannel wiener filtering and spatial prediction. IEEE International Conference on Acoustics, Speech, and Signal Processing, 129–132.
Bernard, W., & Fa-Long, L., (2003). Microphone arrays for hearing aids: An overview. Speech Communication, 39, 139–146.
Emanuel, A. P., Habets & Jacob B. (2013). A two-stage beam forming approach for noise reduction and dereverberation. IEEE Transactions on Audio, Speech and language processing, 21(5).
Volkmar., H. (2002). Comparison of advanced monaural and binaural noise reduction algorithms for hearing aids. IEEE International Conference on Acoustics, Speech, and Signal Processing, 4008 – 4011.
Hendriks, R. C., & Gerkmann, T. (2011). Estimation of the noise correlation matrix. IEEE International Conference on Acoustics, Speech, and Signal Processing, 4740–4743.
Doclo, S., & Moonen, M. (2005). On the output SNR of the speech-distortion weighted multichannel wiener filter. IEEE Signal Processing Letters, 12, 809–811.
Doclo, S., Spriet, A., Wouters, J., & Moonen, M. (2007). Frequency-domain criterion for speech distortion weighted multichannel wiener filter for robust noise reduction. Speech Communication, 49 (8), 636–656.
Klasen, T., Van den Bogaert, T., Moonen, M., & Wouters, J. (2005). Preservation of interaural time delay for binaural hearing aids through multichannel wiener filtering based noise reduction. IEEE International Conference on Acoustics Speech and Signal Processing, 29-32.
Simon, D., Van den Bogaert, T., Wouters, J., & Moonen, M. (2007). Comparison of reduced–bandwidth MWF based noise reduction algorithms for binaural hearing aids. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.
Cornelis, B., Doclo, S., Van den Bogaert, T., Wouters, J., & Moonen, M. (2010). Theoretical analysis of binaural multi-microphone noise reduction techniques. IEEE Tranactions on Audio, Speech and Language Processing, 18 (2), 342-355.
Doclo, S., Dong, R., Klasen, T. J., Wouters, J., Haykin, S., & Moonen, M. (2005). Extension of the multi-channel Wiener Filter with ITD cues for noise reduction in binaural hearing aids. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 70-73.
Hilkhuysen, G., Gaubitch, N., Brookes, M., & Huckvale, M. (2012). Effects of noise suppression on intelligibility: Dependency on signal-to-noise ratios. Journal of Acoustical Society of America, 131(1), 531-539.
Yegnanarayana, B., Carlos, A., Hynek, H., & Satyanarayana Murthy, P. (1999). Speech enhancement using linear prediction residual. Speech Communication, 28(1), 25-42.
Jean-Baptiste, M., Liesbeth, R., Jan, W., & Marc, M. (2006). Comparison of adaptive noise reduction algorithms in dual microphone hearing aids. Speech Communication, 48(8), 957-970.
Hu, Y., & Loizou, P. (2007). A comparative intelligibility study of single-microphone noise reduction algorithms. Journal of Acoustical Society of America, 122(3), 1777–1786.
Chen, J., Benesty, J., & Huang, Y. (2008). A minimum distortion noise reduction algorithm with multiple microphones. IEEE Transactions on Audio, Speech, and Language Processing, 16(3), 481–493.
Chen, J., Benesty, J., Huang, Y., & Doclo, S. (2006). New insights into the noise reduction Wiener filter. IEEE Transactions on Audio, Speech, and Language Processing, 14(4), 1218–1234.
Kim, G., Lu, Y., Hu, Y., & Loizou, P. (2009). An algorithm that improves speech intelligibility in noise for normal-hearing listeners. Journal of the Acoustical Society of America, 126(3), 1486-1494.
Dendrinos, M., Bakamidis, S., & Carayannis, G. (1991). Speech enhancement from noise: A regenerative approach. Speech Communication, 10(2), 45–57.
Jeff, V., B., & Jan, W. (1998). An adaptive noise canceller for hearing aids using two nearby microphones. Journal of Acoustical Society of America, 103(6), 3621-3626.
Glasberg, B., R., & Moore, B., C., J. (1986). Auditory filter shapes in subjects with unilateral and bilateral cochlear impairments, Journal of Acoustical Society of America, 79(1), 1020-1033.
Kulkarni, P., N., Pandey, P., C., & Jangamashetti, D., S. (2009). Multi-band frequency compression for sensorineural hearing impairment. 16th IEEE International Conference on Digital Signal Processing, 322-327.
Kulkarni, P., N., Pandey, P., C., & Jangamashetti, D., S. (2009). Multi-band frequency compression for reducing the effects of spectral masking. International Journal of Speech Technology, 10(4), 219-227.
Kulkarni, P., N., Pandey, P., C., & Jangamashetti, D., S. (2012). Multi-band frequency compression for improving speech perception by listeners with moderate sensorineural hearing loss. Speech Communication, 5(3), 341-350.
Novlene, Z., Zied, L., Noureddine, E. (2009). Speech enhancement using auditory spectral attenuation. 17th European Signal Processing Conference.
Kulkarni, P., N., & Pandey, P., C. (2006). Perceptually balanced filter response for binaural dichotic presentation to reduce the effect of spectral masking. Journal of Acoustical Society of America, 120(5), 32-53.
Abd El-Fattah, M., A., Dessouky, M., I., Diab, S., M., & Abd El-samie, F., E. (2008). Speech enhancement using an adaptive wiener filtering approach. Progress in Electromagnetics Research. l4, 167-184.
Zwicker, E. W. (1961). Subdivision of audible frequency range into critical bands (Freqenzgruppen). Journal of Acoustical Society of America, 33(2), 248-248.
Noisy Speech Corpus, http://ecs.utdallas.edu/loizou/speech/noizeus
Loizou, P. C., (2007). Speech enhancement: Theory and practice. CRC Press, 2007.
Kim Ngo, Simon Doclo, Ann Spriet, Marc Moonen, Jan Wouters and Soren Holdt Jensen. (2008). An integrated approach for noise reduction and dynamic range compressionin hearing aids, 16th European Signal Processing Conference , Lausanne, Switzerland, August 25-29, 2008.