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Download PDF by Bernd Iser: Bandwidth Extension of Speech Signals

By Bernd Iser

Bandwidth Extension of Speech signs offers dialogue on diverse methods for effective and powerful bandwidth extension of speech signs whereas acknowledging the impact of noise corrupted real-world indications. The e-book describes the idea and techniques for caliber enhancement of fresh speech indications and distorted speech indications corresponding to those who have passed through a band obstacle, for example, in a mobilephone community. difficulties and the respective options are mentioned with reference to various techniques. the various ways are evaluated and robustness matters for a real-time implementation are coated to boot. The publication comprises subject matters concerning speech coding, trend- / speech reputation, speech enhancement, records and electronic sign processing in general.

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5. Block diagram of MFCC computation The mel-spectrum and afterwards the MFCCs used in this book have been computed following the block diagram in Fig. 5 which represents the standard procedure described in the literature [Deller Jr. 00, Rabiner 93]. 85) n=0 with Ωk = 2πk . 86) Then the absolute value of the spectrum is filtered by a triangular shaped mel-filterbank which can be seen in Fig. 6 for an example with Nmel = 12 mel-filters. After the mel-filterbank the spectral components Smel ejΩk are reduced to the order of the filterbank.

5. 1 Tube Model One possibility of modeling the vocal tract comprises the use of a tube [Vary 98]. This is one of the first attempts to describe a model for the speech production process and is briefly described here for historical reasons. 38) ∂x where 0 denotes the density of air in the tube, p the pressure, and v the sound particle velocity. As a rule, closed-form solutions for this problem are not available. If we assume a model corresponding to Fig. 36) for each tube segment with a constant cross-sectional area Ai to v=− ∂ 2 Φi 1 ∂ 2 Φi = 2 .

Part of the difference that may be observable in Fig. 12 between the estimated and the real pitch is due to the order of the DFT. For the speech spectrogram in Fig. 12 a DFT with a much higher resolution has been used as for the pitch estimation is achievable with the used block length. An example for the distributions of the values for ACI and ACM can be found in Appendix E. Alternatively the pitch estimation can be performed in the frequency domain. Here we compare a set of products consisting of equidistant frequency bins of the spectrum of the excitation signal.

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