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
