Abstract
We developed a real-time wideband speech codec adopting a wavelet packet based methodology. The transform domain coefficients were first quantized by means of a mid-tread uniform quantizer and then encoded with an arithmetic coding. In the first step the wavelet coefficients were quantized by using a psycho-acoustic model. The second step was carried out by adapting the probability model of the quantized coefficients frame by frame by means of a competitive neural network. The neural network was trained on the TIMIT corpus and his weights updated in real-time during the compression in order to model better the speech characteristics of the current speaker. The coding/decoding algorithm was first written in C and then optimised on the TMS320C6000 DSP platform.
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Pasero, E., Montuori, A. (2002). Real-Time Perceptual Coding of Wideband Speech by Competitive Neural Networks. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_18
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DOI: https://doi.org/10.1007/3-540-45808-5_18
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