An algorithm able to classify pathological and normal voice signals based on Daubechies discrete wavelet transform (DWT-db) and support vector machines (SVM) classifier is presented. DWT-db is used for time-frequency analysis giving quantitative evaluation of signal characteristics to identify pathologies in voice signals, particularly nodules in vocal folds, of subjects with different ages for both male and female. After using a linear prediction coefficients (LPC) filter, the signals mean square values of a particular scale from wavelet analysis are entries to a nonlinear least square support vector machine (LS-SVM) classifier, which leads to an adequate larynx pathology classifier which over 95% of classification accuracy.
"A method based on MPEG-7 audio low level is proposed in  for the extraction of features that can be classified by support vector machine (SVM). Similarly, SVM classifiers have also been used in the features extraction based on wavelet transform, nonlinear analysis of temporal series, and information theory     . Mel-frequency cepstral coefficients and linear prediction cepstral coefficients are used as acoustic features in  associated with a strategy based on combining classifiers with Gaussian mixture model, hidden Markov model (HMM), and SVM. "
[Show abstract][Hide abstract] ABSTRACT: This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classification. With this new architecture, the classification process of vocal pathologies becomes much more simple and efficient.
"Our research question is: Can we keep all the features and use feature interactions to our advantage? Recently, wavelet kernels have been investigated in certain applications including regression , voice classification , and biomarker discovery in protein structures , bringing in the reach framework of wavelet analysis from signal processing. As one attempt with a Haar wavelet in information retrieval highlights , there is one fundamental problem with existing wavelet kernels when it comes to classification: in order to make such wavelet kernels operational, a relation (traditionally temporal or spatial) is assumed between subsequent features that describe the data instances. "
[Show abstract][Hide abstract] ABSTRACT: Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L<sub>2</sub> space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.
IEEE Transactions on Pattern Analysis and Machine Intelligence 11/2011; 33(10-33):2039 - 2050. DOI:10.1109/TPAMI.2011.28 · 5.78 Impact Factor
"Many of the recent algorithms described in the literature use wavelets as powerful tools in the feature-extraction stage. Wavelets are applied in various forms, such as the discrete wavelet transform (DWT)   , continuous wavelet transform (CWT)    and wavelet packets    . From the classification point of view, the efficiency of the overall system depends on the appropriateness of both the extracted features and the classification method applied. "
[Show abstract][Hide abstract] ABSTRACT: The presence of abnormalities in the vocal system affects the quality of the voice and changes its characteristics. Digital analysis of pathological voices can be an effective and non-invasive tool for the detection of such alterations. This paper proposes a wavelet-based method to distinguish between normal and disordered voices. Wavelet filter banks are used in conjunction with support vector machines, as feature extractors and classifiers, respectively. Orthogonal filter banks are implemented using a highly efficient structure known as "lattice" that parameterizes filter banks and produces a few parameters. The overall problem is to find these parameters such that perfect classification is achieved. To search for such parameters, a genetic algorithm with a fitness function corresponding to the classification result is applied. Simulation is done on the KAY database (a comprehensive database including 710 normal and pathological voice signals, developed by the Massachusetts Eye and Ear Infirmary Voice and Speech Lab), and one additional test set. It is observed that a genetic algorithm is able to find the filter bank parameters such that a 100% correct classification rate is achieved in classifying normal and pathological voices when the test is performed on both databases.
Computers in Biology and Medicine 09/2011; 41(9):822-8. DOI:10.1016/j.compbiomed.2011.06.019 · 1.24 Impact Factor
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