82 research outputs found
Models and Analysis of Vocal Emissions for Biomedical Applications
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
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
Models and Analysis of Vocal Emissions for Biomedical Applications
The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy
Computational Modelling and Analysis of Vibrato and Portamento in Expressive Music Performance
PhD, 148ppVibrato and portamento constitute two expressive devices involving continuous
pitch modulation and is widely employed in string, voice, wind music instrument
performance. Automatic extraction and analysis of such expressive features
form some of the most important aspects of music performance research and
represents an under-explored area in music information retrieval. This thesis
aims to provide computational and scalable solutions for the automatic extraction
and analysis of performed vibratos and portamenti. Applications of the
technologies include music learning, musicological analysis, music information
retrieval (summarisation, similarity assessment), and music expression synthesis.
To automatically detect vibratos and estimate their parameters, we propose
a novel method based on the Filter Diagonalisation Method (FDM). The FDM
remains robust over short time frames, allowing frame sizes to be set at values
small enough to accurately identify local vibrato characteristics and pinpoint
vibrato boundaries. For the determining of vibrato presence, we test two alternate
decision mechanisms—the Decision Tree and Bayes’ Rule. The FDM
systems are compared to state-of-the-art techniques and obtains the best results.
The FDM’s vibrato rate accuracies are above 92.5%, and the vibrato
extent accuracies are about 85%.
We use the Hidden Markov Model (HMM) with Gaussian Mixture Model
(GMM) to detect portamento existence. Upon extracting the portamenti, we
propose a Logistic Model for describing portamento parameters. The Logistic
Model has the lowest root mean squared error and the highest adjusted Rsquared
value comparing to regression models employing Polynomial and Gaussian
functions, and the Fourier Series.
The vibrato and portamento detection and analysis methods are implemented
in AVA, an interactive tool for automated detection, analysis, and visualisation
of vibrato and portamento. Using the system, we perform crosscultural
analyses of vibrato and portamento differences between erhu and violin
performance styles, and between typical male or female roles in Beijing opera
singing
Voice source characterization for prosodic and spectral manipulation
The objective of this dissertation is to study and develop techniques to decompose the speech signal into its two main
components: voice source and vocal tract. Our main efforts are on the glottal pulse analysis and characterization. We want to
explore the utility of this model in different areas of speech processing: speech synthesis, voice conversion or emotion detection
among others. Thus, we will study different techniques for prosodic and spectral manipulation. One of our requirements is that
the methods should be robust enough to work with the large databases typical of speech synthesis. We use a speech production
model in which the glottal flow produced by the vibrating vocal folds goes through the vocal (and nasal) tract cavities and its
radiated by the lips. Removing the effect of the vocal tract from the speech signal to obtain the glottal pulse is known as inverse
filtering. We use a parametric model fo the glottal pulse directly in the source-filter decomposition phase.
In order to validate the accuracy of the parametrization algorithm, we designed a synthetic corpus using LF glottal parameters
reported in the literature, complemented with our own results from the vowel database. The results show that our method gives
satisfactory results in a wide range of glottal configurations and at different levels of SNR. Our method using the whitened
residual compared favorably to this reference, achieving high quality ratings (Good-Excellent). Our full parametrized system
scored lower than the other two ranking in third place, but still higher than the acceptance threshold (Fair-Good).
Next we proposed two methods for prosody modification, one for each of the residual representations explained above. The first
method used our full parametrization system and frame interpolation to perform the desired changes in pitch and duration. The
second method used resampling on the residual waveform and a frame selection technique to generate a new sequence of
frames to be synthesized. The results showed that both methods are rated similarly (Fair-Good) and that more work is needed in
order to achieve quality levels similar to the reference methods.
As part of this dissertation, we have studied the application of our models in three different areas: voice conversion, voice quality
analysis and emotion recognition. We have included our speech production model in a reference voice conversion system, to
evaluate the impact of our parametrization in this task. The results showed that the evaluators preferred our method over the
original one, rating it with a higher score in the MOS scale. To study the voice quality, we recorded a small database consisting of
isolated, sustained Spanish vowels in four different phonations (modal, rough, creaky and falsetto) and were later also used in
our study of voice quality. Comparing the results with those reported in the literature, we found them to generally agree with
previous findings. Some differences existed, but they could be attributed to the difficulties in comparing voice qualities produced
by different speakers. At the same time we conducted experiments in the field of voice quality identification, with very good
results. We have also evaluated the performance of an automatic emotion classifier based on GMM using glottal measures. For
each emotion, we have trained an specific model using different features, comparing our parametrization to a baseline system
using spectral and prosodic characteristics. The results of the test were very satisfactory, showing a relative error reduction of
more than 20% with respect to the baseline system. The accuracy of the different emotions detection was also high, improving
the results of previously reported works using the same database. Overall, we can conclude that the glottal source parameters
extracted using our algorithm have a positive impact in the field of automatic emotion classification
Models and Analysis of Vocal Emissions for Biomedical Applications
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
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