Application of Wavelets in Speech Processing

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Wavelet - Wikipedia

Purchase Instant Access. View Preview. Learn more Check out. Abstract This paper presents a method of using wavelet analysis for speech coding and synthesis by rule. Citing Literature.

Wavelet Transform

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Lec 54 - Introduction to wavelets

Browse All Figures Return to Figure. Previous Figure Next Figure. Email or Customer ID. Forgot password? In the previous figure, most of the DWT coefficients are zero, which indicates that the wavelet transform is a useful method to represent signals sparsely and compactly. Therefore, you usually use the DWT in some signal compression applications. Transient features are sudden changes or discontinuities in a signal.

A transient feature can be generated by the impulsive action of a system and frequently implies a causal relationship to an event. For example, heartbeats generate peaks in an electrocardiogram ECG signal.

Transient features generally are not smooth and are of short duration. Because wavelets are flexible in shape and have short time durations, the wavelet signal processing methods can capture transient features precisely.

Mit multirate signal processing

The following figure shows an ECG signal and the peaks detected with the wavelet transform-based method. This method locates the peaks of the ECG signal precisely. Signals usually contain both low-frequency components and high-frequency components. Low-frequency components vary slowly with time and require fine frequency resolution but coarse time resolution.

High-frequency components vary quickly with time and require fine time resolution but coarse frequency resolution. You need to use a multiresolution analysis MRA method to analyze a signal that contains both low- and high-frequency components.

Isolated English alphabet speech recognition using wavelet cepstral coefficients and neural network

Wavelet signal processing is naturally an MRA method because of the dilation process. The following figure shows the wavelets with different dilations and their corresponding power spectra. The Wavelets graph contains three wavelets with different scales and translations. The Power Spectra of Wavelets graph shows the power spectra of the three wavelets, where a and u represent the scale and shift of the wavelets, respectively. The previous figure shows that a wavelet with a small scale has a short time duration, a wide frequency bandwidth, and a high central frequency.


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This figure also shows that a wavelet with a large scale has a long time duration, a narrow frequency bandwidth, and a low central frequency. The time duration and frequency bandwidth determine the time and frequency resolutions of a wavelet, respectively. A long time duration means coarse time resolution.

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A theory for multi resolution signal decomposition: the wavelet representation. Ubeyil ED. Combined neural network model employing wavelet coefficients for ECG signals classification. EEG feature extraction based on wavelet packet decomposition for brain computer interface. Wavelet Analysis and its Applications. Twitter Facebook Linkedin Youtube. User Username Password Remember me. Volume 10, Issue 1, January-March Volume 9, Issue 3, July-September Volume 9, Issue 2, April-June Volume 9, Issue 1, January-March Volume 8, Issue 3, July-September Volume 8, Issue 2, April-June Volume 8, Issue 1, January-March Volume 7, Issue 3, July-September Volume 7, Issue 2, April-June Volume 7, Issue 1, January-March Volume 6, Issue 3, July-September


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