A Nonnegative Subspace Approach for Packet Loss Concealment

• Autorzy: Huang J. , Zhang Y. , Zhang X. , Xia Z.

Warianty tytułu

PL

Metoda nieujemnej podprzestrzeni stosowana w przypadku straty pakietu

• Języki publikacji: EN

Abstrakty

EN

This paper presents a nonnegative subspace approach for packet loss concealment problem. The magnitude spectrogram of speech signal is projected onto nonnegative subspace using nonnegative matrix factorization algorithm. Consequently, packet loss concealment problem is transformed to linear interpolation of the projective coefficients in nonnegative subspace. Simulation examples, objective tests show that packet loss concealment in the nonnegative subspace results in improved perceptual quality of speech compared to popular packet loss concealment algorithms.

PL

Zaprezentowano metodę subprzestrzeni dla rozwiązania problemu straty pakietu. Spektrogram amplitudowy sygnału mowy .jest poddawany projekcji do nieujemnej podprzestrzeni przy wykorzystaniu macierzy faktoryzacji. W rezultacie problem staje się możliwy do liniowej interpolacji. Osiągnięto dostrzegalną poprawę jakości przetwarzania sygnału mowy.

Słowa kluczowe

EN

electrical technology; electrical power engineering; packet loss concealment; nonnegative subspace; nonnegative matrix factorization

PL

elektrotechnika; elektroenergetyka; strata pakietu; nieujemna przestrzeń

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2011
• Tom: R. 87, nr 10
• Strony: 350--354
• Opis fizyczny: Bibliogr. 16 poz., il., tabl., wykr.

Twórcy

autor

Huang J.

autor

Zhang Y.

autor

Zhang X.

autor

Xia Z.

• Postgraduate Team 2, Haifu Xiang 1, Baixia District, Nanjing, China, 210007,

Bibliografia

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• [4] Esfandiar Zavarehei and Saeed Vaseghi, “Interpolation of Lost Speech Segments Using LP-HNM Model With Codebook Post-Processing” IEEE Trans. Multimedia, 10(2008), No.3, 493-502
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• [6] D. D. Lee and H. S. Seung, “Algorithms for nonnegative matrix factorization,” Neural Inf. Process. Syst., 2001, 556-562
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• [9] Alexey Ozerov and Cédric Févotte, “Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation,” IEEE Trans. Audio, Speech, Lang. Process., 18(2010), No. 3, 550-563
• [10] Nancy Bertin, Roland Badeau, and Emmanuel Vincent, “Enforcing harmonicity and smoothness in bayesian nonnegative matrix factorization applied to polyphonic music transcription,” IEEE Trans. Audio, Speech, Lang. Process., 18(2010), No. 3, 538-549
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• [13] Zhe Chen, Cichocki, A., Rutkowski, T.M., “Constrained non- Negative Matrix Factorization Method for EEG Analysis in Early Detection of Alzheimer Disease,” Proc. ICASSP, 2006, 109-112
• [14] Stone J. V. “Blind source separation using temporal predictability,” Neural Computation, 13(2001), 1559-1574
• [15] Appendix I: A High Quality Low-Complexity Algorithm for Packet Loss Concealment with G.711. ITU-T Recommendation- G.711, 1999.
• [16] Perceptual Evaluation of Speech Quality (PESQ), an Objective Method for End-to-End Speech Quality Assessment of Narrowband Telephone Networks and Speech Codecs, ITU-T Recommendation-862, 2001.