John Ashley Burgoyne

A Meta-analysis of Timbre Perception Using Nonlinear Extensions to CLASCAL

Seeking to identify the constituent parts of the multidimensional auditory attribute that musicians know as timbre, music psychologists have made extensive use of multidimensional scaling ( mds ), a statistical technique for visualising the geometric spaces implied by perceived dissimilarity. mds is also well known in the machine learning community, where it is used as a basic technique for dimensionality reduction. We adapt a nonlinear variant of mds that is popular in machine learning, Isomap, for use in analysing psychological data and re-analyse three earlier experiments on human perception of timbre. Isomap is designed to eliminate undesirable nonlinearities in the input data in order to reduce the overall dimensionality; our results show that it succeeds in these goals for timbre spaces, compressing the output onto well-known dimensions of timbre and highlighting the challenges inherent in quantifying differences in spectral shape.

Goal-directed evaluation for the improvement of optical music recognition on early music prints

Optical music recognition (OMR) systems are promising tools for the creation of searchable digital music libraries. Using an adaptive OMR system for early music prints based on hidden Markov models, we leverage an edit distance evaluation metric to improve recognition accuracy. Baseline results are computed with new labeled training and test sets drawn from a diverse group of prints. We present two experiments based on this evaluation technique. The first resulted in a significant improvement to the feature extraction function for these images. The second is a goal-directed comparison of several popular adaptive binarization algorithms, which are often evaluated only subjectively. Accuracy increased by as much as 55% for some pages, and the experiments suggest several avenues for further research.

Creating a large-scale searchable digital collection from printed music materials

In this paper we present our work towards developing a large-scale web application for digitizing, recognizing (via optical music recognition), correcting, displaying, and searching printed music texts. We present the results of a recently completed prototype implementation of our workflow process, from document capture to presentation on the web. We discuss a number of lessons learned from this prototype. Finally, we present some open-source Web 2.0 tools developed to provide essential infrastructure components for making searchable printed music collections available online. Our hope is that these experiences and tools will help in creating next-generation globally accessible digital music libraries.

Reducing costs for digitising early music with dynamic adaptation

Optical music recognition (OMR) enables librarians to digitise early music sources on a large scale. The cost of expert human labour to correct automatic recognition errors dominates the cost of such projects. To reduce the number of recognition errors in the OMR process, we present an innovative approach to adapt the system dynamically, taking advantage of the human editing work that is part of any digitisation project. The corrected data are used to perform MAP adaptation, a machine-learning technique used previously in speech recognition and optical character recognition (OCR). Our experiments show that this technique can reduce editing costs by more than half.