scholarly journals A chord distance metric based on the Tonal Pitch Space and a key-finding method for chord annotation sequences

2019 ◽  
Author(s):  
Lucas Marques
Keyword(s):  
Information Retrieval ◽  
Success Rate ◽  
Musical Piece ◽  
Classic Problem ◽  
Theoretical Approaches ◽  
Chord Distance ◽  
Complementary Techniques ◽  
Music Information ◽  
Tonal Pitch ◽  
Finding Method

Music Information Retrieval (MIR) is a growing field of research concerned about recovering and generating useful information about music in general. One classic problem of MIR is key-finding, which could be described as the activity of finding the most stable tone and mode of a determined musical piece or a fragment of it. This problem, however, is usually modeled for audio as an input, sometimes MIDI, but little attention seems to be given to approaches considering musical notations and musictheory. This paper will present a method of key-finding that has chord annotations as its only input. A new metric is proposed for calculating distances between tonal pitch spaces and chords, which will be later used to create a key-finding method for chord annotations sequences. We achieve a success rate from 77.85% up to 88.75% for the whole database, depending on whether or not and how some parameters of approximation are configured. We argue that musical-theoretical approaches independent of audio could still bring progress to the MIR area and definitely could be used as complementary techniques.

scholarly journals Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06257
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Rubiel Vargas-Cañas ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Music Information Retrieval ◽  
Machine Learning Classification ◽  
Information Retrieval Methods ◽  
Music Information

Musical Features that Aid Sleep

2020 ◽  
pp. 102986492097216
Author(s):  
Gaelen Thomas Dickson ◽  
Emery Schubert
Keyword(s):  
Information Retrieval ◽  
Online Survey ◽  
Music Information Retrieval ◽  
Rhythmic Activity ◽  
Main Frequency ◽  
Middle Range ◽  
Major Mode ◽  
Information Retrieval Methods ◽  
Music Information ◽  
Selection Of

Background: Music is thought to be beneficial as a sleep aid. However, little research has explicitly investigated the specific characteristics of music that aid sleep and some researchers assume that music described as generically sedative (slow, with low rhythmic activity) is necessarily conducive to sleep, without directly interrogating this assumption. This study aimed to ascertain the features of music that aid sleep. Method: As part of an online survey, 161 students reported the pieces of music they had used to aid sleep, successfully or unsuccessfully. The participants reported 167 pieces, some more often than others. Nine features of the pieces were analyzed using a combination of music information retrieval methods and aural analysis. Results: Of the pieces reported by participants, 78% were successful in aiding sleep. The features they had in common were that (a) their main frequency register was middle range frequencies; (b) their tempo was medium; (c) their articulation was legato; (d) they were in the major mode, and (e) lyrics were present. They differed from pieces that were unsuccessful in aiding sleep in that (a) their main frequency register was lower; (b) their articulation was legato, and (c) they excluded high rhythmic activity. Conclusion: Music that aids sleep is not necessarily sedative music, as defined in the literature, but some features of sedative music are associated with aiding sleep. In the present study, we identified the specific features of music that were reported to have been successful and unsuccessful in aiding sleep. The identification of these features has important implications for the selection of pieces of music used in research on sleep.

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