Simulating music learning with autonomous listening agents : entropy, ambiguity and context /
Reis, Ben Y.
Cambridge : University of Cambridge, Computer Laboratory,
1999.
Abstract: "Music learning describes the gradual process of acculturation through which listeners in different cultures develop diverse sets of musical preferences and intuitions.  This dissertation describes Maestro, a system designed over the course of this research to simulate certain aspects of music listening and learning.  In order to maintain the unbiased flexibility necessary for handling music from different styles, Maestro does not incorporate any a priori style-specific knowledge into its design.  Instead, Maestro is based on a bottom-up approach that maximises the use of the perceptual information present in a performance.  Maestro's operation involves four stages: it first segments a musical performance on-line according to perceptual cues (segmentation), and constructs an appropriate model of the performance (modelling), based on the context modelling paradigm.  This model is simultaneously used to generate expectations about upcoming events (prediction) and to interpret events once they have arrived (parsing).  Ambiguity is an essential aspect of music listening, especially in the context of learning, and can cause multiple hypotheses of interpretation to arise.  A novel multi-agent methodology is developed and incorporated into Maestro for generating, maintaining, and reconciling these hypotheses.  An information theoretic approach, based on measuring two types of entropy, is used to objectively evaluate the system's relative prediction performance.  It is also found that entropy, along with a measure of agent activation, is useful for identifying and classifying different types of ambiguity.  Experiments performed with a collection of 100 Bach chorale melodies provide a basis for comparison with previous machine modelling research and with data from human subjects.  A much larger collection of roughly 8,000 folk songs from different cultures enables significant large-scale and pan-stylistic music learning experiments to be performed.  Perceptually guided segmentation is argued to yield more cognitively realistic context models than other methods, and is also empirically shown to yield more efficient models for prediction.  Additionally, an adaptive modelling strategy allows appropriate multiple-step-ahead predictions to be generated.  Finally, a distributed, agent-based parsing methodology is developed and implemented.  The system provides insights into what implications certain theories from cognitive musicology have when put into practice.  Maestro's flexible design together with the range of experiments performed and the diverse corpus of musical data enable a thorough and systematic machine-simulated study of key aspects of music learning to be carried out."
"September 1999."
Thesis (Ph. D.)--University of Cambridge, 1999.
Abstract: "Music learning describes the gradual process of acculturation through which listeners in different cultures develop diverse sets of musical preferences and intuitions.  This dissertation describes Maestro, a system designed over the course of this research to simulate certain aspects of music listening and learning.  In order to maintain the unbiased flexibility necessary for handling music from different styles, Maestro does not incorporate any a priori style-specific knowledge into its design.  Instead, Maestro is based on a bottom-up approach that maximises the use of the perceptual information present in a performance.  Maestro's operation involves four stages: it first segments a musical performance on-line according to perceptual cues (segmentation), and constructs an appropriate model of the performance (modelling), based on the context modelling paradigm.  This model is simultaneously used to generate expectations about upcoming events (prediction) and to interpret events once they have arrived (parsing).  Ambiguity is an essential aspect of music listening, especially in the context of learning, and can cause multiple hypotheses of interpretation to arise.  A novel multi-agent methodology is developed and incorporated into Maestro for generating, maintaining, and reconciling these hypotheses.  An information theoretic approach, based on measuring two types of entropy, is used to objectively evaluate the system's relative prediction performance.  It is also found that entropy, along with a measure of agent activation, is useful for identifying and classifying different types of ambiguity.  Experiments performed with a collection of 100 Bach chorale melodies provide a basis for comparison with previous machine modelling research and with data from human subjects.  A much larger collection of roughly 8,000 folk songs from different cultures enables significant large-scale and pan-stylistic music learning experiments to be performed.  Perceptually guided segmentation is argued to yield more cognitively realistic context models than other methods, and is also empirically shown to yield more efficient models for prediction.  Additionally, an adaptive modelling strategy allows appropriate multiple-step-ahead predictions to be generated.  Finally, a distributed, agent-based parsing methodology is developed and implemented.  The system provides insights into what implications certain theories from cognitive musicology have when put into practice.  Maestro's flexible design together with the range of experiments performed and the diverse corpus of musical data enable a thorough and systematic machine-simulated study of key aspects of music learning to be carried out."
Includes bibliographical references.
Artificial intelligence
Machine learning.
Electronic data processing