Wavelet packets based features selection for voiceless plosives classification

Lukasik, Ewa

doi:10.1109/icassp.2000.859053

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…Other approaches have compared time series at various resolutions, but comparing the time series focusing on their time-frequency properties using the continuous wavelet transform as we did has not, to our knowledge, been done (Lukasik 2000, Keogh et al 2001). …”

Section: Time Series Clusteringmentioning

confidence: 99%

Analysing multiple time series and extending significance testing in wavelet analysis

Rouyer¹,

Fromentin²,

Stenseth³

et al. 2008

Mar. Ecol. Prog. Ser.

101

105

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Section: Time Series Clusteringmentioning

confidence: 99%

Analysing multiple time series and extending significance testing in wavelet analysis

Rouyer¹,

Fromentin²,

Stenseth³

et al. 2008

Mar. Ecol. Prog. Ser.

101

105

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“…Recently, wavelet-based features have been reported to perform better in some of the phoneme recognition problems Datta, 2000, 2001;Lukasik, 2000;Mallat, 1997). In this paper, we report the recognition performance of wavelet-based features and compare it with the LPC-and MFCC-based features (Atal, 1974;Markel and Gray, 1982;O'Shaughnessy, 1986).…”

Section: Introductionmentioning

confidence: 97%

Wavelet-based, speaker-independent isolated Hindi digit recognition

Gazi

Farooq

Khan

et al. 2008

IJICT

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In this paper, Admissible Wavelet Packet (AWP)-based features are proposed for the recognition of isolated Hindi digit. AWPs are used to design a set of filter banks to follow the Mel scale. Finally, a Hidden Markov Model (HMM) is developed for recognition. The database used was collected from 48 subjects with a repetition of each digit five times. Features based on both Linear Predictive Coefficients (LPCs) as well as Mel Frequency Cepstral Coefficients (MFCCs) were extracted and their performance compared with the AWP-based features. It was found that the recognition performance using AWP-based features was superior when compared with LPC-and MFCC-based features.

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“…The best-basis algorithm was developed for compact signal representations, whereas the LDB algorithm was developed for classification applications. The LDB algorithm has been successfully used to address few realworld classification problems (SPOONER, 2001;CHRISTIAN, 2002;LUKASIK, 2000).…”

Section: Introductionmentioning

confidence: 99%

Feature analysis of pathological speech signals using local discriminant bases technique

Umapathy

Krishnan

2005

Med. Biol. Eng. Comput.

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Speech is an integral part of the human communication system. Various pathological conditions affect the vocal functions, inducing speech disorders. Acoustic parameters of speech are commonly used for the assessment of speech disorders and for monitoring the progress of the patient over the course of therapy. In the last two decades, signal-processing techniques have been successfully applied in screening speech disorders. In the paper, a novel approach is proposed to classify pathological speech signals using a local discriminant bases (LDB) algorithm and wavelet packet decompositions. The focus of the paper was to demonstrate the significance of identifying the signal subspaces that contribute to the discriminatory characteristics of normal and pathological speech signals in a computationally efficient way. Features were extracted from target subspaces for classification, and time-frequency decomposition was used to eliminate the need for segmentation of the speech signals. The technique was tested with a database of 212 speech signals (51 normal and 161 pathological) using the Daubechies wavelet (db4). Classification accuracies up to 96% were achieved for a two-group classification as normal and pathological speech signals, and 74% was achieved for a four-group classification as male normal, female normal, male pathological and female pathological signals.

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Wavelet packets based features selection for voiceless plosives classification

Cited by 16 publications

References 5 publications

Analysing multiple time series and extending significance testing in wavelet analysis

Analysing multiple time series and extending significance testing in wavelet analysis

Wavelet-based, speaker-independent isolated Hindi digit recognition

Feature analysis of pathological speech signals using local discriminant bases technique

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