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Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. Those involved in MIR may have a background in academic musicology, psychoacoustics, psychology, signal processing, informatics, machine learning, optical music recognition, computational intelligence, or some combination of these.
The main academic full-text databases are open archives or link-resolution services, although others operate under different models such as mirroring or hybrid publishers. Such services typically provide access to full text and full-text search, but also metadata about items for which no full text is available.
Computational musicology includes any disciplines that use computation in order to study music. It includes sub-disciplines such as mathematical music theory, computer music, systematic musicology, music information retrieval, digital musicology, sound and music computing, and music informatics. [2] As this area of research is defined by the ...
Publication data: 1972, Journal of the Royal Statistical Society, Series B, volume 34, pages 187–220. JSTOR 2985181 Description: Seminal paper introducing semi-parametric proportional hazards models for survival data Importance: Topic creator, Breakthrough, Influence. The Statistical Analysis of Failure Time Data
Cognitive musicology can be differentiated from other branches of music psychology via its methodological emphasis, using computer modeling to study music-related knowledge representation with roots in artificial intelligence and cognitive science. The use of computer models provides an exacting, interactive medium in which to formulate and ...
List of datasets in computer vision and image processing. Outline of machine learning. v. t. e. These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning ...
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods " to "understand and analyze actual phenomena " with data. [5] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. [6]
Sound and music computing. Sound and music computing (SMC) is a research field that studies the whole sound and music communication chain from a multidisciplinary point of view. By combining scientific, technological and artistic methodologies it aims at understanding, modeling and generating sound and music through computational approaches.