In this paper, a robust DWPT based adaptive bock algorithm with
modified threshold for denoising the sounds of musical instruments shehnai,
dafli and flute is proposed. The signal is first segmented into multiple blocks
depending upon the minimum mean square criteria in each block, and then
thresholding methods are used for each block. All the blocks obtained after
denoising the individual block are concatenated to get the final denoised
signal. The discrete wavelet packet transform provides more coefficients than
the conventional discrete wavelet transform (DWT), representing additional
subtle detail of the signal but decision of optimal decomposition level is very
important. When the sound signal corrupted with additive white Gaussian noise
is passed through this algorithm, the obtained peak signal to noise ratio
(PSNR) depends upon the level of decomposition along with shape of the wavelet.
Hence, the optimal wavelet and level of decomposition may be different for each
signal. The obtained denoised signal with this algorithm is close to the
Cite this paper
R. Sharma and V. Pyara, "A Robust Denoising Algorithm for Sounds of Musical Instruments Using Wavelet Packet Transform," Circuits and Systems
, Vol. 4 No. 7, 2013, pp. 459-465. doi: 10.4236/cs.2013.47060
 M. Lang, H. Guo, J. E. Odegard, C. S. Burrus and R. O. Wells, “Noise Reduction Using an Undecimated Discrete Wavelet Transform,” IEEE Signal Processing Letters, Vol. 3, No. 1, 1996, pp. 10-12.
 J. Yang, Y. Wang, W. Xu and Q. Dai, “Image and Video Denoising Using Adaptive Dual Tree Discrete Wavelet Packets,” IEEE Transaction on Circuit and Systems for Video Technology, Vol. 19, No. 5, 2009, pp. 642-655.
 B. J. Shankar and K. Duariswamy, “Wavelet Based Block Matching Process: An efficient Audio Denoising Technique,” European Journal of Scientific Research, Vol. 48, No. 1, 2010, p. 16.
 R. Sharma and V. P. Pyara, “A Novel Approach to Synthesize Sounds of Some Indian Musical Instruments Using DWT,” International Journal of Computer Applications, Vol. 45, No. 13, 2012, pp. 19-22.
 R. Sharma and V. P. Pyara, “A Comparative Analysis of Mean Square Error Adaptive Filter Algorithms for Generation of Modified Scaling and Wavelet Function,” International Journal of Engineering Science and Technology, Vol. 4, No. 4, 2012, pp. 1396-1401.
 J. Yu and D. C. Liu, “Thresholding Based Wavelet Packet Methods for Doppler Ultrasound Signal Denoising,” IFMBE Proceedings Springer Verlag Berlin Heidelberg, Vol. 19, No. 9, 2008, pp. 408-412.
 T. Mourad, S. Lotfi and C. Adnen, “Spectral Entropy Employment in Speech Enhancement Based on Wavelet Packet,” International Journal of Computer and Information Engineering, Vol. 1, No. 7, 2007, pp. 404-411.
 N. S. Nehe and R. S. Holambe, “DWT and LPC Based Feature Extraction Methods for Isolated Word Recognition,” EURASIP Journal of Audio, Speech and Music Processing, Vol. 7, No. 1, 2012, pp. 1-7.http://dx.doi.org/10.1186/1687-4722-2012-7
 D. Kwon, M. Vannucci, J. J. Song, J. Jeong and R. M. Pfeiffer, “A Novel Wavelet Based Thresholding Method for the Pre-Processing of Mass Spectrometry Data That Accounts for Heterogeneous Noise,” Proteomics, Vol. 8, No. 15, 2008, pp. 3019-3029.
 Y. Ren, M. T. Johnson and J. Tao, “Perceptually Motivated Wavelet Packet Transform for Bio-Acoustic Signal Enhancement,” Journal of Acoustic Society of America, Vol. 124, No. 1, 2008, pp. 316-327.
 K. Ramchandran and M. Vetterli, “Best Wavelet Packet Bases in a Rate-distortion Sense,” IEEE Transaction on Image Processing, Vol. 2, No. 2, 1993, pp. 160-175.
 D. L. Donoho and I. M. Johnstone, “Adapting to Unknown Smoothness via Wavelet Shrinkage,” Journal of the American Statistical Association, Vol. 90, No. 432, 1995, pp. 1200-1224.
 S. G. Chang, B. Yu and M. Vetterli, “Adaptive Wavelet Thresholding for Image Denoising and Compression,” IEEE Transaction on Image Processing, Vol. 9, No. 9, 2000, pp. 1532-1546.
 J. Berger, R. R. Coifman and J. G Maxim, “Removing Noise from Music Using Local Trigonometric Bases and Wavelet Packets,” Journal of The Audio Engineering Society, Vol. 42, No. 10, 1994, pp. 808-818.
 M. T. Johnson, X. Yuan and Y. Ren, “Speech Signal Enhancement through Adaptive Wavelet Thresholding,” Speech Communication, Vol. 49, No. 2, 2007, pp. 123-133.