138 research outputs found
A Cross-Cultural Analysis of Music Structure
PhDMusic signal analysis is a research field concerning the extraction of meaningful information
from musical audio signals. This thesis analyses the music signals from the note-level
to the song-level in a bottom-up manner and situates the research in two Music information
retrieval (MIR) problems: audio onset detection (AOD) and music structural
segmentation (MSS).
Most MIR tools are developed for and evaluated on Western music with specific musical
knowledge encoded. This thesis approaches the investigated tasks from a cross-cultural
perspective by developing audio features and algorithms applicable for both Western and
non-Western genres. Two Chinese Jingju databases are collected to facilitate respectively
the AOD and MSS tasks investigated.
New features and algorithms for AOD are presented relying on fusion techniques. We
show that fusion can significantly improve the performance of the constituent baseline
AOD algorithms. A large-scale parameter analysis is carried out to identify the relations
between system configurations and the musical properties of different music types.
Novel audio features are developed to summarise music timbre, harmony and rhythm for
its structural description. The new features serve as effective alternatives to commonly
used ones, showing comparable performance on existing datasets, and surpass them on
the Jingju dataset. A new segmentation algorithm is presented which effectively captures
the structural characteristics of Jingju. By evaluating the presented audio features and
different segmentation algorithms incorporating different structural principles for the
investigated music types, this thesis also identifies the underlying relations between audio
features, segmentation methods and music genres in the scenario of music structural
analysis.China Scholarship Council
EPSRC C4DM Travel Funding,
EPSRC Fusing Semantic and Audio Technologies for Intelligent Music Production and
Consumption (EP/L019981/1),
EPSRC Platform Grant on Digital Music (EP/K009559/1),
European Research Council project CompMusic, International Society for Music Information Retrieval Student Grant,
QMUL Postgraduate Research Fund,
QMUL-BUPT Joint Programme Funding
Women in Music Information Retrieval Grant
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 mechanismsāthe Decision Tree and Bayesā Rule. The FDM
systems are compared to state-of-the-art techniques and obtains the best results.
The FDMās 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
An review of automatic drum transcription
In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often deļ¬ning the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classiļ¬cation of drum sound events by computational methods is considered to be an important and challenging research problem in the broader ļ¬eld of Music Information Retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term Automatic Drum Transcription(ADT).This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-speciļ¬c challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on Nonnegative Matrix Factorization and Recurrent Neural Networks. We explain the methodsā technical details and drum-speciļ¬c variations and evaluate these approaches on publicly available datasets with a consistent experimental setup. Finally, the open issues and under-explored areas in ADT research are identiļ¬ed and discussed, providing future directions in this ļ¬el
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The perceptual and cognitive roles of the motor system
The motor system in the brain is crucial in allowing us to successfully move aroundin our environment, interact with people and objects, and execute finely controlled motorcommands. While most of the early neuroscience research on these regions tends to focuson these āmainā functions, over the last few decades evidence has been surfacing thatpoints to a more broadly integrated role for the motor system. Many recent findingssuggest that it is also of importance in many other aspects of human cognition, fromlanguage and thought to social cognition and, as I discuss in depth in the followingsections, many perceptual processes. In the following chapters, I outline and compareexisting prediction-based and simulation-based theories for motor system involvement inperception. I also describe experiments I completed investigating motor systeminvolvement in written language perception, music perception, and action observation.Furthermore, I discuss how these processes relate to conceptual learning and recall. Insummary, a vast literature points to the motor system proper not being a neural networkthat is only good for controlling and planning our actions. As we develop the vocabularyof the field to use terms like āaction-perception loopsā and discuss these processes as lessseparable than previously considered, perhaps we should also reconsider the term āmotorsystemā to reflect its diverse roles in sensorimotor prediction
Lawrence University Course Catalog, 2007-2008
https://lux.lawrence.edu/coursecatalogs/1005/thumbnail.jp
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