Although audio to score alignment is a classic Music Information Retrieval problem, it has not been defined uniquely with the scope of musical scenarios representing its core. The absence of a unified vision makes it difficult to pinpoint its state-of-the-art and determine directions for improvement. To get past this bottleneck, it is necessary to consolidate datasets and evaluation methodologies to allow comprehensive benchmarking. In our review of prior work,we demonstrate the extent of variation ...
Although audio to score alignment is a classic Music Information Retrieval problem, it has not been defined uniquely with the scope of musical scenarios representing its core. The absence of a unified vision makes it difficult to pinpoint its state-of-the-art and determine directions for improvement. To get past this bottleneck, it is necessary to consolidate datasets and evaluation methodologies to allow comprehensive benchmarking. In our review of prior work,we demonstrate the extent of variation in problem scope, datasets, and evaluation practices across audio to score alignment research. To circumvent the high cost of creating large-scale datasets with various instruments, styles, performance conditions, and musician proficiency from scratch, the research community could generate ground truth approximations from non-audio to score alignment datasets which include a temporal mapping between a music score and its corresponding audio. We show a methodology for adapting the Aligned Scores and Performances dataset, created originally for beat tracking and music transcription. We filter the dataset semi-automatically by applying a set of Dynamic Time Warping based Audio to Score Alignment methods using out-of-the-box Chroma and Constant-Q Transform extraction algorithms, suitable for the characteristics of the piano performances of the dataset. We use the results to discuss the limitations of the generated ground truths and data adaptation method. While the adapted dataset does not provide the necessary diversity for solving the initial problem, we conclude with ideas for expansion, and identify future directions for curating more comprehensive datasets through data adaptation, or synthesis.
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