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Bandwidth Extension of Speech Signals by Bernd Iser

By Bernd Iser

Bandwidth Extension of Speech indications presents dialogue on diverse methods for effective and strong bandwidth extension of speech indications whereas acknowledging the impression of noise corrupted real-world indications. The ebook describes the idea and techniques for caliber enhancement of fresh speech indications and distorted speech signs reminiscent of those who have passed through a band issue, for example, in a mobilephone community. difficulties and the respective options are mentioned with reference to varied ways. the several methods are evaluated and robustness matters for a real-time implementation are lined besides. The e-book contains subject matters regarding speech coding, trend- / speech acceptance, speech enhancement, records and electronic sign processing in general.

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Bandwidth Extension of Speech Signals

Bandwidth Extension of Speech indications presents dialogue on assorted methods for effective and strong bandwidth extension of speech indications whereas acknowledging the impression of noise corrupted real-world indications. The e-book describes the idea and techniques for caliber enhancement of unpolluted speech indications and distorted speech signs similar to those who have passed through a band issue, for example, in a cell community.

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3. 2 Extension Using Non-Linear Characteristics 57 Enb(e jΩk−i) Enb(e jΩi) Ωi Frequency (a) Convolution. Magnitude Magnitude Eˆbb(e jΩk) Ω0 Frequency Ωk (b) Result of convolution. Fig. 4. (a) and (b) illustrate the effect of the convolution in the frequency domain. Note that this is only a schematic illustration. The resulting line spectrum typically exhibits a coloration due to the respective non-linear characteristic that has been applied and due to the aliasing that occurs depending on the sampling rate and the effective bandwidth unintentional we can simply once again apply a predictor-error filter as described in Sect.

The value of the kurtosis for the unvoiced utterance however lies below 3. Experiments using the local kurtosis showed difficulties in a robust voiced/unvoiced classification and have therefore been abandoned within this work. 5 Spectral Centroid The spectral centroid is a measure that indicates where most of the power of a speech segment is spectrally located. The spectral centroid is defined as N/2 xs,sc = k · S ejΩk k=0 N/2 N 2 +1 k=0 . 3 Scalar Speech Features 43 Fig. 13. (a) and (b) Show one block of a voiced and an unvoiced sequence of a speech signal and their respective local kurtosis values; (c) and (d) show a speech signal and its spectrogram, respectively.

P }, and possesses only zeros in the z-plane, whereas the AR-model would be characterized by bi = 0 for i ∈ {1, . . , P }, and is therefore a straight recursive filter and possesses only poles in the z-plane. 5) we observe that the approach of modeling the vocal tract transfer function by using an AR-model has already been addressed under the term linear predictive analysis in Sect. 1. Since the approach presented in Sect. 1 is very intuitive and has already been described in detail we will equivalently use the terms LP-coefficients and AR-coefficients for the rest of the book.

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