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This paper presents an automatic method for simultaneous detection and elimination of impulsive noises from audio signals. The method is based on the sparse signal decomposition (SSD) with overcomplete hybrid dictionary (OHD) matrix including impulse and sinusoidal waveforms derived from discrete impulse, sine and cosine functions, and 1-norm optimization algorithm. The SSD technique decomposes an...
In this paper, we have employed learned dictionaries to compute sparse representation of speech utterances, which will be used to reduce the footprint of unit selection based speech synthesis (USS) systems. Speech database labeled at phoneme level is used to obtain multiple examples of the same phoneme, and all the examples (of each phoneme) are then used to learn a single overcomplete dictionary...
This paper proposes an approach based on compressed sensing to reduce the footprint of speech corpus in unit selection based speech synthesis (USS) systems. It exploits the observation that speech signal can have a sparse representation (in suitable choice of basis functions) and can be estimated effectively using the sparse coding framework. Thus, only few significant coefficients of the sparse vector...
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