By Bernd Iser
Bandwidth Extension of Speech indications presents dialogue on varied methods for effective and strong bandwidth extension of speech indications whereas acknowledging the impression of noise corrupted real-world signs. The publication describes the idea and techniques for caliber enhancement of unpolluted speech signs and distorted speech indications equivalent to those who have gone through a band dilemma, for example, in a phone community. difficulties and the respective suggestions are mentioned with reference to varied methods. the various techniques are evaluated and robustness matters for a real-time implementation are coated to boot. The publication contains themes with regards to speech coding, development- / speech acceptance, speech enhancement, statistics and electronic sign processing in general.
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Extra info for Bandwidth Extension of Speech Signals
Fig. 15. Examples for several energy based features 48 3 Analysis Techniques for Speech Signals In this section some energy based features have been presented. The only feature that has been used for classiﬁcation tasks within this work is the highpass energy to lowpass energy ratio. It has been used for a voiced/unvoiced classiﬁcation of the current utterance (see Sect. 2). The relative frame energy or SNR has been employed within the control part of a system for bandwidth extension to switch oﬀ the extension if a certain threshold has been exceeded to avoid malclassiﬁcations for the spectral envelope estimation and thereby bothersome artifacts.
6 Itakura–Saito Distance The following distance measure was ﬁrst published by Itakura and Saito and is one of the most popular distance measures in speech signal processing [Rabiner 93] π dIS 1 A, Aˆ = 2π −π ˆ jΩ )|2 ˆ jΩ )|2 |A(e |A(e − ln − 1 dΩ. 129) However it should be noted that, in parallel to the Itakura distance, the Itakura–Saito distance is not symmetric and therefore does not satisfy the constraints of a metric. 7 Other Spectral Distance Measures The spectral distortion measures presented above do not take the human auditory system into account and do not penalize spectral overestimation higher than underestimation due to their symmetry [Nilsson 01].
9 an example for a voiced and an unvoiced block of N = 256 samples is given. However the analysis of the zero crossing rate should be combined with a speech activity detector since in the presence of noise it is unlikely to be able to diﬀerentiate between a fricative and noise for example. 2 Gradient Index The so-called gradient index is deﬁned similarly to the zero crossing rate. The main diﬀerences are the dependence on the magnitude and the use of the so-called change of direction. 98) s2 (n) n=m where 1 |ψ(n) − ψ(n − 1)| .