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论文类型：会议论文

发表时间：2010-09-26

收录刊物：EI、CPCI-S、Scopus

页面范围：1101-1104

关键字：speech bandwidth expansion; wavelet transform modulus maxima; artificial neural network

摘要：A novel approach to speech bandwidth expansion based on wavelet transform modulus maxima vector mapping is proposed. By taking advantage of the similarity of the modulus maxima vectors between narrowband and wideband wavelet-analyzed signals a neural network mapping structure can be established to perform bandwidth expansion given only the narrowband version of speech. Since the proposed algorithm works on the time-domain waveforms it offers a flexibility of variable-length frame selection that facilitates low delay and potentially data-dependent speech segment processing to further improve the speech quality. Evaluations based on both objective and subjective measures show that the proposed bandwidth expansion approach results in high-quality synthesized wideband speech with little perceivable distortion from the original wideband speech signals.