NDLI: Incremental Dataset Definition for Large Scale Musicological Research

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Incremental Dataset Definition for Large Scale Musicological Research

Content Provider	ACM Digital Library
Author	Benetos, Emmanouil Tidhar, Dan Dumon, Edouard Cherla, Srikanth Weyde, Tillman Wolff, Daniel
Abstract	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.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450330022
DOI	10.1145/2660168.2660176
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-12
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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