Computational modelling and analysis of vibrato and portamento in expressive music performance

Vibrato 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. Statement of Originality I, Luwei Yang, confirm that the research included within this thesis is my own work or that where it has been carried out in collaboration with, or supported by others, that this is duly acknowledged below and my contribution indicated. Previously published material is also acknowledged below. I attest that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge break any UK law, infringe any third party’s copyright or other Intellectual Property Right, or contain any confidential material. I accept that the College has the right to use plagiarism detection software to check the electronic version of the thesis. I confirm that this thesis has not been previously submitted for the award of a degree by this or any other university. The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author. Signature: Luwei Yang Date: 19 September 2016