This paper explains a study conducted based on wavelet packet transform techniques. In this paper the key idea underlying the construction of wavelet packet analysis (WPA) with various wavelet basis sets is elaborated. Since wavelet packet decomposition can provide more precise frequency resolution than wavelet decomposition the implementation of one dimensional wavelet packet transform and their usefulness in time signal analysis and synthesis is illustrated. A mother or basis wavelet is first chosen for five wavelet filter families such as Haar, Daubechies (Db4), Coiflet, Symlet and dmey. The signal is then decomposed to a set of scaled and translated versions of the mother wavelet also known as time and frequency parameters. Analysis and synthesis of the time signal is performed around 8 seconds to 25 seconds. This was conducted to determine the effect of the choice of mother wavelet on the time signals. Results are also prepared for the comparison of the signal at each decomposition level. The physical changes that are occurred during each decomposition level can be observed from the results. The results show that wavelet filter with WPA are useful for analysis and synthesis purpose. In terms of signal quality and the time required for the analysis and synthesis, the Haar wavelet has been seen to be the best mother wavelet. This is taken from the analysis of the signal to noise ratio (SNR) value which is around 300 dB to 315 dB for the four decomposition levels.
In this paper we analyze the combination of speech and FIR filter design aspect to achieve good results in speech quality. A new approach in the time domain based on the least P th norm is presented to extract maximum information that represents speech. The aim of this paper is to improve the perceived quality of speech through the introduction of least P th norm algorithm that attenuates speech contaminated with noise. This approach relates to a filter bank structure and a method for filtering and separating an information signal into different bands, particularly for filtering and separation of speech signals. Then the desired signal is reconstructed from the independent components representing every band. This approach differs from the traditional approaches since no priori knowledge of the noise statistics is required, instead the noise signals are only assumed to have finite energy. Since the estimation criterion for the filter design is to minimize the worst possible amplification of the estimation error signal in terms of modeling errors and additive noise, this approach is highly robust and appropriate in practical speech analysis and synthesis. This paper presents a least P th approach to the optimal design of FIR digital filter banks in the minimax sense for speech analysis and synthesis. The signal to noise ratio (SNR) of around 50-60 dB is achieved with various speech samples.
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