Paper with Guidelines for Multimodal Datasets Published at TISMIR

M. Gotham, B. Bemman, and I. Vatolkin: Towards an ‘Everything Corpus’: A Framework and Guidelines for the Curation of More Comprehensive Multimodal Music Data. Transactions of the International Society for Music Information Retrieval, 8(1), pp. 70-92.

Abstract: Current state-of-the-art methods for instrument and pitch detection in polyphonic music often require large datasets and long training times; resources which are sparse in the field of music information retrieval, presenting a need for unsupervised alternative methods that do not require such prerequisites. We present a modification to an evolutionary algorithm for polyphonic music approximation through synthesis that uses spectral information to initialise populations with probable pitches. This algorithm can perform joint instrument and pitch detection on polyphonic music pieces without any of the aforementioned constraints. Sets of tuples of (instrument, style, pitch) are graded with a COSH distance fitness function and finally determine the algorithm’s instrument and pitch labels for a given part of a music piece. Further investigation into this fitness function indicates that it tends to create false positives which may conceal the true potential of our modified approach. Regardless of that, our modification still shows significantly faster convergence speed and slightly improved pitch and instrument detection errors over the baseline algorithm on both single onset and full piece experiments.