Wavelet transform and artificial neural networks applied to voice disorders identification

Carvalho, Raphael Torres Santos; Cavalcante, Charles C.; Cortez, Paulo César

doi:10.1109/nabic.2011.6089256

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…Some of the previous works [9][10][11][12]17,20,21,23,24,27,29, for the vocal fold pathology classification problem have been analyzed from the dataset and classifier points of view.…”

Section: Additional Filementioning

confidence: 99%

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A novel hybrid of genetic algorithm and ANN for developing a high efficient method for vocal fold pathology diagnosis

Majidnezhad

2015

J AUDIO SPEECH MUSIC PROC.

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In this paper, an initial feature vector based on the combination of the wavelet packet decomposition (WPD) and the Mel frequency cepstral coefficients (MFCCs) is proposed. For optimizing the initial feature vector, a genetic algorithm (GA)-based approach is proposed and compared with the well-known principal component analysis (PCA) approach. The artificial neural network (ANN) with the different learning algorithms is used as the classifier. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different learning algorithms and the different feature vectors (the initial and the optimized ones). Finally, a hybrid of the ANN with the 'trainscg' training algorithm and the genetic algorithm is proposed for the vocal fold pathology diagnosis. Also, the performance of the proposed method is compared with the recent works. The experiments' results show a better performance (the higher classification accuracy) of the proposed method in comparison with the others.

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“…Some of the previous works [9][10][11][12]17,20,21,23,24,27,29, for the vocal fold pathology classification problem have been analyzed from the dataset and classifier points of view.…”

Section: Additional Filementioning

confidence: 99%

“…Also, some of the well-known classifiers for the classification phase in the previous works were used such as support vector machine (SVM) [16][17][18][19], Gaussian mixture model (GMM) [20][21][22], artificial neural network (ANN) [23][24][25], and hidden Markov model (HMM) [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid of genetic algorithm and ANN for developing a high efficient method for vocal fold pathology diagnosis

Majidnezhad

2015

J AUDIO SPEECH MUSIC PROC.

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“…In phoniatrics time-frequency representations (primarily spectrograms and scaleograms) are used for diagnosis of diseases of the vocal organs [1, 5,9,19,20,11,35,43,45]. The authors [21] presented an overview of the methodology of automatic detection of pathological changes in the voice.…”

Section: Introductionmentioning

confidence: 99%

“…The authors show that even small pathological changes in the vocal folds are visible on the time-frequency plane, which allows sensitive detection of affects and helps to diagnose. Authors of many works in order to identify diseases and pathological changes in the voice used a discrete wavelet transformation DWT [1,41] and support vector machine-based classification method as feature classification tools [1, 5,6,11]. In scientific work [1, 21,44] demonstrated that the most effective algorithm (100% recognition efficiency) is a system composed of wavelet packet transforms along with feature dimension reduction by linear discriminant analysis and a support vector machine-based classification method.…”

Section: Introductionmentioning

confidence: 99%

Advanced Time-Frequency Representation in Voice Signal Analysis

Mika¹,

Jóźwik²

2018

Adv. Sci. Technol. Res. J.

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The most commonly used time-frequency representation of the analysis in voice signal is spectrogram. This representation belongs in general to Cohen's class, the class of time-frequency energy distributions. From the standpoint of properties of the resolution, spectrogram representation is not optimal. In Cohen class representations are known which have a better resolution properties. All of them are created by smoothing the Wigner-Ville'a distribution characterized by the best resolution, however, the biggest harmful interference. The used smoothing functions decide about a compromise between the properties of resolution and eliminating harmful interference term. Another class of time-frequency energy distributions is the affine class of distributions. From the point of view of readability of analysis of the best properties are known so called Redistribution of energy caused by the use of a general methodology referred to as reassignment to any time-frequency representation. Reassigned distributions efficiently combine a reduction of the interference terms provided by a well adapted smoothing kernel and an increased concentration of the signal components.

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“…The accuracy reaches 87% for Reinke's edema and 86% for nodules using 80% of the data for training and 20% for independent testing. In [14] the authors use jitter in the wavelets components with SVM (Support Vector Machines) to perform distinction between nodules and Reinke's edema. Using a similar dataset as in [13], 82% accuracy has been achieved.…”

Section: Introductionmentioning

confidence: 99%

Continuous Speech Classification Systems for Voice Pathologies Identification

Cordeiro

Meneses

Fonseca

2015

IFIP Advances in Information and Communication Technology

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Part 8: Signal Processing in MedicineInternational audienceVoice pathologies identification using speech processing methods can be used as a preliminary diagnostic. The aim of this study is to compare the performance of sustained vowel /a/ and continuous speech task in identification systems to diagnose voice pathologies. The system recognizes between three classes consisting of two different pathologies sets and healthy subjects. The signals are evaluated using MFCC (Mel Frequency Cepstral Coefficients) as speech signal features, applied to SVM (Support Vector Machines) and GMM (Gaussian Mixture Models) classifiers. For continuous speech, the GMM system reaches 74% accuracy rate while the SVM system obtains 72% accuracy rate. For the sustained vowel /a/, the accuracy achieved by the GMM and the SVM is 66% and 69% respectively, a lower result than with continuous speech

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Wavelet transform and artificial neural networks applied to voice disorders identification

Cited by 14 publications

References 12 publications

A novel hybrid of genetic algorithm and ANN for developing a high efficient method for vocal fold pathology diagnosis

A novel hybrid of genetic algorithm and ANN for developing a high efficient method for vocal fold pathology diagnosis

Advanced Time-Frequency Representation in Voice Signal Analysis

Continuous Speech Classification Systems for Voice Pathologies Identification

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