This paper compares wavelet and short time Fourier transform based techniques for single channel speech signal noise reduction. Despite success of wavelet denoising of images, it has not yet been widely used for removal of noise in speech signals. We explored how to extend this technique to speech denoising, and discovered some problems in this endeavor. Experimental comparison with large amount test data has been performed. Our results have shown that although the Fourier domain methods still has the superiority, wavelet based alternatives can be very close, and enormous different configurations can still be tried out for possible better solutions.
A modified run-length algorithm with Peano-Hilbert scanning is described. In this approach, quantization is integrated within the run-length calculation, and is controlled by a radius parameter. A zero value of radius will produce lossless coding, while greater than zero will produce lossy coding. The Peano-Hilbert curve scans a 2D array with local plane filling priority. Compared with the standard raster and zigzag scanning, the Peano-Hilbert scanning enlarges pixel covariance as distance between pixels increases. It, therefore, enhances run-length compression ratio as degree of lossy increases. Output of the run-length encoder is further entropy coded using a decomposed Huffman encoder. The proposed method can be applied directly to a raw image or combined with DPCM, wavelet or subband transforms. If the transform is also implemented with integer arithmetic, such as in the lossless JPEG or reversible wavelet transform, a unified lossy and lossless compression is achieved. The method is simpler than the JPEG standard, and yet achieves roughly equivalent performance when combined with DPCM. Decoding is very fast as no de-quantization procedure is needed. Experiments with various types of images have shown its speed advantage and compression efficiency.
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