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  1. Proceedings of the 3rd International workshop on Digital Libraries for Musicology (DLfM 2016)
  2. MORTY: A Toolbox for Mode Recognition and Tonic Identification
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Representing and Linking Music Performance Data with Score Information
Approaches to handwritten conductor annotation extraction in musical scores
MORTY: A Toolbox for Mode Recognition and Tonic Identification
Document Analysis for Music Scores via Machine Learning
In Collaboration with In Concert: Reflecting a Digital Library as Linked Data for Performance Ephemera
Exploring J-DISC: Some Preliminary Analyses
A standard format proposal for hierarchical analyses and representations
Digitizing musical scores: Challenges and opportunities for libraries
Data Generation and Multi-Modal Analysis for Recorded Operatic Performance
Mining metadata from the web for AcousticBrainz
The Music Addressability API: A draft specification for addressing portions of music notation on the web

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MORTY: A Toolbox for Mode Recognition and Tonic Identification

Content Provider ACM Digital Library
Author Şentürk, Sertan Karakurt, Altuğ Serra, Xavier
Abstract In the general sense, mode defines the melodic framework and tonic acts as the reference tuning pitch for the melody in the performances of many music cultures. The mode and tonic information of the audio recordings is essential for many music information retrieval tasks such as automatic transcription, tuning analysis and music similarity. In this paper we present MORTY, an open source toolbox for mode recognition and tonic identification. The toolbox implements generalized variants of two state-of-the-art methods based on pitch distribution analysis. The algorithms are designed in a generic manner such that they can be easily optimized according to the culture-specific aspects of the studied music tradition. We test the generalized methodology systematically on the largest mode recognition dataset curated for Ottoman-Turkish makam music so far, which is composed of 1000 recordings in 50 modes. We obtained 95.8%, 71.8% and 63.6% accuracy in tonic identification, mode recognition and joint mode and tonic estimation tasks, respectively. We additionally present recent experiments on Carnatic and Hindustani music in comparison with several methodologies recently proposed for raga/raag recognition. We prioritized the reproducibility of our work and provide all of our data, code and results publicly. Hence we hope that our toolbox would be used as a benchmark for future methodologies proposed for mode recognition and tonic identification, especially for music traditions in which these computational tasks have not been addressed yet.
Starting Page 9
Ending Page 16
Page Count 8
File Format PDF
ISBN 9781450347518
DOI 10.1145/2970044.2970054
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-08-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Hindustani music Mode recognition Carnatic music Ottoman-turkish makam music Reproducibility Toolbox Tonic identification K-nearest neighbors classification Open source software Pitch class distribution
Content Type Text
Resource Type Article
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