Hello I am Pramod Kumar working on project speaker identification using wavelet packet transform. I would to get the matlab implementation for the project. Can you please help me in this regard? If so, I will be able to complete my project . Thank You in advance.
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Subband processing is based on dividing the frequency range into M (subband) segments, which span the entire range. Each sub-band is processed independently, as required by the specific application.
The subband coding decomposes the input signal into different frequency bands. After the input decomposes into its components, we can use the most appropriate encoding technique for each component to improve compression performance. Decompose a signal into components by applying frequency selective filtering. Then select the best coding technique that best suits each component (subjectively and objectively). The subband coding applications are UIT-T Voice Coding G.722 High quality voice coding at 64/56/48 kbps. The interest in signal processing is long before computers. While people have tried to send or receive information through electronic means such as telegraphs, telephones, television, radar, etc., they have realized that these signals may be affected by the system used to acquire, transmit or process them . Sometimes these systems are imperfect and introduce noise, distortion or other artifacts. Understanding the effects of these systems and finding ways to correct them is the basis of signal processing. This article describes the analysis side of the system implemented using the free software language SCILAB. Subband coding (SBC) is a type of transformation coding. A signal is divided into a number of different frequency bands and each encodes independently. It allows data compression by discarding information about frequencies that are masked. The result is different from the original signal, but if the discarded information is carefully chosen, the difference will not be noticeable, or more important. The basic concept of "Language Frequency Domain" methods is to divide speech into frequency components by a filter bank (subband coding), or by a suitable transformation (transform coding), and then encode them Using adaptive PCM. Recent developments and examples of the adaptive encoder of the "Vocoder" encoding for low bitrate applications [9] are also discussed. In the subband coding of speech signals using deciphering and interpolation, we propose a channel quadrature mirror filter with low pass filter, high pass filter, decimators and interpolators, to perform the subband coding of Voice signals in the digital domain. The results show that the proposed structure significantly reduces error and achieves a considerable improvement in performance compared to delta-modulation coding systems. The subband encoder reduces and controls the quantizing noise.
