Extracción de parámetros de señales de voz usando técnicas de análisis en tiempo - frecuencia

En este trabajo se presenta la extracción de características de señales de voz basada en transformada wavelet. Las características se pueden clasificar en los tipos acústico y de representación. Dentro de las características acústicas aparecen la frecuencia fundamental y la de medida de ruido de señales de voz. Para la estimación de la frecuencia fundamental se aplica un nuevo método, el cual usa la correlación de distancias entre las escalas de descomposición en lugar de usar la correlación de posiciones de máximos locales en las escalas. Para la obtener la medida de ruido de las señales de voz se usa un método basado en la transformada wavelet packet. Para la obtención de las características de representación se usan varia estrategias, la más simple de ella es usando la transformada wavelet diádica, y las otras se basan en el diccionario de bases generado a partir de la transformada wavelet packet, entre ellas Local Discriminant Bases. / Abstract. This work present methods for feature extraction of speech signals based on wavelet transform. The features can be organized in two categories, acoustic and representation. Present a new method for pitch estimation and use the wavelet packet transform for noise estimation. For extraction of representation features use the dyadic wavelet transform and schemes based on wavelet packet transform, por ejemplo, Local Discriminant Bases. This features are used for pathological voices classification and are evaluated using Linear Discriminant Analysis. As preprocessing technique we use an algorithm for voiced/unvoiced decision an later apply pitch estimation. The results are compared with other methods. An improvement pitch detection algorithm based on the Wavelet Transform (WT) of speech signal is proposed. The method obtains a value of the fundamental frequency for each pitch period, is described and evaluated. In contrast with other methods, which chooses maximums if they occur in two adjacent wavelet coefficient scales, distances between adjacent local maximums are chosen for each scale. This method is computationally inexpensive and through real speech experiments shows that it is both accurate and robust to noise.Maestrí

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition

Title on author’s file: Classification of mechanomyogram signal using wavelet packet transform and singular value decomposition for multifunction prosthesis control2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p

A novel hybrid method for vocal fold pathology diagnosis based on russian language

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neighbours) and the different feature vectors (the initial and the optimized ones). Finally, a hybrid of the ensemble of decision tree and the genetic algorithm is proposed for vocal fold pathology diagnosis based on Russian Language. The experimental results show a better performance (the higher classification accuracy and the lower response time) of the proposed method in comparison with the others. While the usage of pure decision tree leads to the classification accuracy of 85.4% for vocal fold pathology diagnosis based on Russian language, the proposed method leads to the 8.5% improvement (the accuracy of 93.9%)

Feature Selection and Non-Euclidean Dimensionality Reduction: Application to Electrocardiology.

Heart disease has been the leading cause of human death for decades. To improve treatment of heart disease, algorithms to perform reliable computer diagnosis using electrocardiogram (ECG) data have become an area of active research. This thesis utilizes well-established methods from cluster analysis, classification, and localization to cluster and classify ECG data, and aims to help clinicians diagnose and treat heart diseases. The power of these methods is enhanced by state-of-the-art feature selection and dimensionality reduction. The specific contributions of this thesis are as follows. First, a unique combination of ECG feature selection and mixture model clustering is introduced to classify the sites of origin of ventricular tachycardias. Second, we apply a restricted Boltzmann machine (RBM) to learn sparse representations of ECG signals and to build an enriched classifier from patient data. Third, a novel manifold learning algorithm is introduced, called Quaternion Laplacian Information Maps (QLIM), and is applied to visualize high-dimensional ECG signals. These methods are applied to design of an automated supervised classification algorithm to help a physician identify the origin of ventricular arrhythmias (VA) directed from a patient's ECG data. The algorithm is trained on a large database of ECGs and catheter positions collected during the electrophysiology (EP) pace-mapping procedures. The proposed algorithm is demonstrated to have a correct classification rate of over 80% for the difficult task of classifying VAs having epicardial or endocardial origins.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113303/1/dyjung_1.pd