Topic
Harmonic wavelet transform
About: Harmonic wavelet transform is a research topic. Over the lifetime, 9602 publications have been published within this topic receiving 247336 citations.
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TL;DR: A version of the resolution of the identity and some properties of FRWPT connected with those of FRFT and WPT are shown.
Abstract: We introduce the concept of the Fractional Wave Packet Transform(FRWPT), based on the idea of the Fractional Fourier Transform(FRFT) and Wave Packet Transform(WPT). We show a version of the resolution of the identity and some properties of FRWPT connected with those of FRFT and WPT.
43 citations
TL;DR: A new class of block transforms is presented, constructed from subband decomposition filter banks corresponding to regular wavelets, which are compared to the discrete cosine transform (DCT).
Abstract: A new class of block transforms is presented. These transforms are constructed from subband decomposition filter banks corresponding to regular wavelets. New transforms are compared to the discrete cosine transform (DCT). Image coding schemes that use the block wavelet transform (BWT) are developed. BWT's can be implemented by fast (O(N log N)) algorithms. >
43 citations
TL;DR: The results indicate that testing signals can be displayed in the time–frequency domain at the same time and then be explored every single time by CWT, especially for changes in frequency content.
42 citations
15 Mar 1994
TL;DR: In this article, the authors presented the difficulties in the use of wavelet transforms for speech processing and showed how the careful selection of the wavelet coefficients can enable the four major categories of speech -voiced speech, plosives, fricatives, and silence -to be identified.
Abstract: We propose the design of a hearing aid based on the wavelet transform. The fast wavelet transform is used to decompose speech into different frequency components. This paper presents the difficulties in the use of wavelet transforms for speech processing and shows how the careful selection of wavelet coefficients can enable the four major categories of speech - voiced speech, plosives, fricatives, and silence - to be identified. With knowledge of these four categories, it is shown how speech can be easily and effectively segmented.
42 citations
TL;DR: In this article, a new wavelet transform within the framework of the local fractional calculus is introduced, and an illustrative example of a LFT is presented, along with an example of an LFT in the context of the LFT calculus.
Abstract: We introduce a new wavelet transform within the framework of the local fractional calculus. An illustrative example of local fractional wavelet transform is also presented.
42 citations