By Bernd Iser

ISBN-10: 1441943366

ISBN-13: 9781441943361

Bandwidth Extension of Speech indications offers dialogue on assorted ways for effective and powerful bandwidth extension of speech signs whereas acknowledging the impression of noise corrupted real-world signs. The ebook describes the idea and strategies for caliber enhancement of fresh speech indications and distorted speech indications corresponding to those who have passed through a band drawback, for example, in a cell community. difficulties and the respective strategies are mentioned with reference to assorted techniques. the various techniques are evaluated and robustness concerns for a real-time implementation are lined to boot. The e-book comprises themes regarding speech coding, development- / speech acceptance, speech enhancement, records and electronic sign processing in general.

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5 enb(n) (e) Tanh characteristic. (f) Benesty characteristic. 5 1 enb(n) (g) Mod. quadratic characteristic. Fig. 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.

98) s2 (n) n=m where 1 |ψ(n) − ψ(n − 1)| . 99) 2 Here ψ(n) denotes the sign of the gradient ψ(n) = sgn {s(n) − s(n − 1)} [Jax 04a]. As well as the zero crossing rate, the gradient index shows higher values for unvoiced speech segments and lower values for voiced ones (see Fig. 10). 3 Fundamental Frequency Another interesting scalar speech feature is the fundamental frequency (pitch frequency or simply pitch). As already mentioned in Chap. 2 it is correlated with the gender of the speaker. One possible approach for extracting the pitch frequency of a speech signal is called autocorrelation method.

Ss,n (P, 0) φss,n (P, 1) φss,n (P, 2) . . 35) is still symmetric (φss,n (i, ) = φss,n ( , i)) but not Toeplitz any more. For the solution of this problem also very efficient algorithms like the so-called Cholesky decomposition exist [Rabiner 93]. Since this book respectively the algorithms presented within comprise only the autocorrelation method, the interested reader is referred to the literature [Markel 76]. Many improvements to the linear predictive analysis concerning the relation between formants and fundamental frequency have been investigated.

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Bandwidth Extension of Speech Signals by Bernd Iser


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