Incremental dataset definition for large scale musicological research

Wolff, D., Tidhar, D., Benetos, E., Dumon, E., Cherla, S. & Weyde, T. (2014). Incremental dataset definition for large scale musicological research. Paper presented at the 1st International Digital Libraries for Musicology workshop, 12-09-2014 - 12-09-2014, London, UK.

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Conducting experiments on large scale musical datasets often requires the definition of a dataset as a first step in the analysis process. This is a classification task, but metadata providing the relevant information is not always available or reliable and manual annotation can be prohibitively expensive. In this study we aim to automate the annotation process using a machine learning approach for classification. We evaluate the effectiveness and the trade-off between accuracy and required number of annotated samples. We present an interactive incremental method based on active learning with uncertainty sampling. The music is represented by features extracted from audio and textual metadata and we evaluate logistic regression, support vector machines and Bayesian classification. Labelled training examples can be iteratively produced with a web-based interface, selecting the samples with lowest classification confidence in each iteration.

We apply our method to address the problem of instrumentation identification, a particular case of dataset definition, which is a critical first step in a variety of experiments and potentially also plays a significant role in the curation of digital audio collections. We have used the CHARM dataset to evaluate the effectiveness of our method and focused on a particular case of instrumentation recognition, namely on the detection of piano solo pieces. We found that uncertainty sampling led to quick improvement of the classification, which converged after ca. 100 samples to values above 98%. In our test the textual metadata yield better results than our audio features and results depend on the learning methods. The results show that effective training of a classifier is possible with our method which greatly reduces the effort of labelling where a residual error rate is acceptable.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © Emmanouil Benetos | ACM 2014. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. Article in press.
Subjects: M Music and Books on Music
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: School of Informatics > Department of Computing

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