Demos and Late-Breaking Session of the Thirteenth International Society for Music Information Retrieval Conference (ISMIR 2012)

Anglade, Amélie; Humphrey, Eric J.; Schmidt, E.; Stober, Sebastian; Sordo, Mohamed

doi:10.1162/comj_r_00171

Cited by 3 publications

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“…Less than four years have passed since the subject of MIIR was first discussed during the “Unconference” (Anglade et al, 2013 ) at the International Society of Music Information Retrieval Conference (ISMIR) in 2012. ISMIR 2016 already featured a well-attended tutorial on the “Introduction to EEG Decoding for Music Information Retrieval Research” and for the first time, the annual seminar on Cognitively based Music Informatics Research (CogMIR) was co-located as a satellite event which drew the attention of many main-conference attendees.…”

Section: Discussionmentioning

confidence: 99%

Toward Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative

Stober

2017

Front. Psychol.

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As an emerging sub-field of music information retrieval (MIR), music imagery information retrieval (MIIR) aims to retrieve information from brain activity recorded during music cognition–such as listening to or imagining music pieces. This is a highly inter-disciplinary endeavor that requires expertise in MIR as well as cognitive neuroscience and psychology. The OpenMIIR initiative strives to foster collaborations between these fields to advance the state of the art in MIIR. As a first step, electroencephalography (EEG) recordings of music perception and imagination have been made publicly available, enabling MIR researchers to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. This paper reports on first results of MIIR experiments using these OpenMIIR datasets and points out how these findings could drive new research in cognitive neuroscience.

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Section: Discussionmentioning

confidence: 99%

Toward Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative

Stober

2017

Front. Psychol.

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“…Their system learnt standard counterpoint rules on voice leading, namely how to avoid open parallels. Other musical applications of ILP include the learning of harmonic rules that express differences between two music corpora, specifically Beatles songs (pop music) and the Real Book (jazz) ( Anglade & Dixon, 2008 ), and music performance rules for piano ( Tobudic & Widmer, 2003 ) and violin ( Ramirez et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina

Anders

Inden

2019

PeerJ Computer Science

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We describe a method for automatically extracting symbolic compositional rules from music corpora. Resulting rules are expressed by a combination of logic and numeric relations, and they can therefore be studied by humans. These rules can also be used for algorithmic composition, where they can be combined with each other and with manually programmed rules. We chose genetic programming (GP) as our machine learning technique, because it is capable of learning formulas consisting of both logic and numeric relations. GP was never used for this purpose to our knowledge. We therefore investigate a well understood case in this study: dissonance treatment in Palestrina’s music. We label dissonances with a custom algorithm, automatically cluster melodic fragments with labelled dissonances into different dissonance categories (passing tone, suspension etc.) with the DBSCAN algorithm, and then learn rules describing the dissonance treatment of each category with GP. Learning is based on the requirement that rules must be broad enough to cover positive examples, but narrow enough to exclude negative examples. Dissonances from a given category are used as positive examples, while dissonances from other categories, melodic fragments without dissonances, purely random melodic fragments, and slight random transformations of positive examples, are used as negative examples.

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Improving Quality of Service for Radio Station Hosting: An Online Recommender System Based on Information Fusion

et al. 2014

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Demos and Late-Breaking Session of the Thirteenth International Society for Music Information Retrieval Conference (ISMIR 2012)

Cited by 3 publications

References 0 publications

Toward Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative

Toward Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative

Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina

Improving Quality of Service for Radio Station Hosting: An Online Recommender System Based on Information Fusion

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