Speech Compression Using Wavelets

Student: Nikhil Rao

Supervisor: Dr. John Homer

Category: Electrical Engineering Thesis Project

Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. This thesis presents a new algorithm to compress speech signals using Discrete Wavelet Transform (DWT) Techniques.

Wavelet analysis is the breaking up of a signal into a set of scaled and translated versions of an original (or mother) wavelet. Taking the wavelet transform of a signal decomposes the original signal into wavelets coefficients at different scales and positions. These coefficients represent the signal in the wavelet domain and all data operations can be performed using just the corresponding wavelet coefficients.

In this thesis a Wavelet based speech coder is implemented in software using Matlab 6’s Wavelet Toolbox. The major issues concerning the design of this Wavelet based speech coder are choosing optimal wavelets for speech signals, decomposition level in the DWT, thresholding criteria for coefficient truncation and efficient encoding of truncated coefficients.

The performance of the wavelet compression scheme on both male and female spoken sentences is compared. On a male spoken sentence the scheme reaches a signal-to-noise ratio of 17.45 db and a compression ratio of 3.88, using a level dependent thresholding approach. A significant advantage of using wavelets for speech coding is that the compression ratio can easily be varied, while most other techniques have fixed compression ratios.

Finally some enhancements to the wavelet compression technique are suggested to improve reconstructed speech signal quality and compression ratios.

Poster Presentation (PDF)

Thesis Document (PDF)

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