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Time normalization in voice analysis

Authors: Y, Qi;

Time normalization in voice analysis

Abstract

The harmonics-to-noise ratio (HNR) has been widely accepted for quantifying the irregular or noise component of voice. HNR, however, is usually inflated by cycle-to-cycle variations of fundamental frequency period because zero padding is used for time normalization of the wavelet. In this study, a new method was developed for analyzing waveform perturbations of voice. In this method, noise components of voice were calculated from the discrepancies between wavelets after they had been optimally aligned in time. The optimal time normalization of wavelets was accomplished using procedures of dynamic time warping (DTW). This method was evaluated using both synthetic and natural voices, and significant reductions in noise were obtained. The harmonics-to-noise ratio obtained using DTW for time normalization was also shown to be independent of fundamental frequency perturbations.

Related Organizations
Keywords

Male, Time Factors, Voice, Humans, Female, Acoustics, Noise, Algorithms

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
34
Top 10%
Top 10%
Average
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