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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Intra-Predictive Switched Split Vector Quantization of Speech Spectra

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Autor(es):
Ramirez, Miguel Arjona [1]
Número total de Autores: 1
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Escola Politecn, Dept Elect Syst Engn, BR-05508 Sao Paulo - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: IEEE SIGNAL PROCESSING LETTERS; v. 20, n. 8, p. 791-794, AUG 2013.
Citações Web of Science: 4
Resumo

Vector quantization (VQ) of speech spectral vectors has been improved by techniques such as split VQ (SVQ), vector transforms and direction switching. This letter proposes Intra-Predictive Switched SVQ (IPSSVQ) with direction switching by a Gaussian Mixture Model (GMM), using at the frame level the prediction-based lower-triangular transform (PLT), which has lower complexity than the Karhunen-Loeve transform (KLT). It is shown that equivalent results to GMM KLT SSVQ may be obtained in the quantization of line spectral frequency (LSF) vectors from wideband speech signals, such as transparent coding throughout the range from 46 bit/frame to 41 bit/frame, with about three-fourths as much operational complexity. (AU)

Processo FAPESP: 12/24789-0 - Análise de sinais de áudio e voz para reconstrução e reconhecimento
Beneficiário:Miguel Arjona Ramírez
Modalidade de apoio: Auxílio à Pesquisa - Regular