{"title":"Spectral Entropy Employment in Speech Enhancement based on Wavelet Packet","authors":"Talbi Mourad, Salhi Lotfi, Ch\u00e9rif Adnen","volume":9,"journal":"International Journal of Electronics and Communication Engineering","pagesStart":2746,"pagesEnd":2754,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/2477","abstract":"
In this work, we are interested in developing a speech denoising tool by using a discrete wavelet packet transform (DWPT). This speech denoising tool will be employed for applications of recognition, coding and synthesis. For noise reduction, instead of applying the classical thresholding technique, some wavelet packet nodes are set to zero and the others are thresholded. To estimate the non stationary noise level, we employ the spectral entropy. A comparison of our proposed technique to classical denoising methods based on thresholding and spectral subtraction is made in order to evaluate our approach. The experimental implementation uses speech signals corrupted by two sorts of noise, white and Volvo noises. The obtained results from listening tests show that our proposed technique is better than spectral subtraction. The obtained results from SNR computation show the superiority of our technique when compared to the classical thresholding method using the modified hard thresholding function based on u-law algorithm.<\/p>\r\n","references":"[1] Y. Ephraim and D. Malah, \"Speech enhancement using a minimum mean\r\nsquare error short time spectral amplitude estimator,\" IEEE Trans. On\r\nAcoust. Speech Signal Processing, vol. 32, no. 6, pp. 1109-1121, 1984.\r\n[2] D.L. Donoho, \"Denoising by soft thresholding,\" IEEE Trans on\r\nInformation Theory, vol. 41, no. 3, pp. 613-627, 1995.\r\n[3] I. M. Johnstone and B. W. Silverman, \"Wavelet threshold estimators for\r\ndata with correlated noise\", J. Roy. Statist. Soc. B, vol. 59, pp. 319-351,\r\n1997.\r\n[4] X. Huang, A. Acero, H. Hon, \"Spoken Language Processing,\" Prentice\r\nHall, p. 474, 2001.\r\n[5] Sungwook Chang, Y. Kwon, Sung-il Yang, and I-jae Kim. \"Speech\r\nenhancement for non-stationary noise environment by adaptive wavelet\r\npacket\" IEEE Tans. pp. 561-564, 2000.\r\n[6] S.S. Chen, Basis Pursuit, Phd Thesis, Standford University, November\r\n1995.\r\n[7] D. Donoho and I. M. Johnstone. \u00d4\u00c7\u00ff\u00d4\u00c7\u00ffIdeal spatial Adaptation via Wavelet\r\nShrinkage-- Biometrika, 41. pp. 425-455, 1994.\r\n[8] Waleed H. Abdulla. \"HMM-based techniques for speech segments\r\nextraction\". ISSN 1058-9244\/02\/S8.00 \u252c\u00ae 2002-IOS Press.\r\n[9] Mohammed BAHOURA and Jean ROUAT \"Wavelet noise reduction:\r\napplication to speech enhancement\". CiteSeer, 2000.\r\n[10] V. Balakrishnan, Nash Borges and Luke Parchment. \"Wavelet denoising\r\nand speech enhancement,\" Spring 2006.\r\n[11] H. Sheikhzadeh and H. Reza Abutalebi. \u00d4\u00c7\u00ff\u00d4\u00c7\u00ffAn improved wavelet-based\r\nspeech enhancement system--. Eurospeech, 2001.\r\n[12] D. Donoho, I. M. Johnstone, G. Kerkyacharian et D.Picard. \"Wavelet\r\nShrinkage: Asymptotia\" Journal of the Royel Statistical Society, Serie\r\nB,57, pp. 3019-3069, 1995.\r\n[13] Jong Won Seok and Keun Sung Bae. \"Speech enhancement with reduction\r\nof noise components in the wavelet domain,\" 0-8186-7919-0\/97 S10.00 \u252c\u00ae\r\n1997 IEEE, pp. 1323-1326.\r\n[14] Pham Van Tuan and Gernot Kubin .\"DWT-Based classification of\r\nAcoustic-Phonetic Classes and Phonetic Units,\" International Conference\r\non Spoken Language Processing (Interspeech-ICSLP) -2004.\r\n[15] S. Mallat, A wavelet tour of signal processing. Academic Press, San Diego,\r\nUSA (1998).\r\n[16] E. Jafer, A.E.Mahdi, \"Wavelet-based Voiced\/Unvoiced classification\r\nalgorithm\", 4th EURASIP Conf., Vol.2, pp.667-672, Croatia,2003.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 9, 2007"}