Musical Instrument Classification and Duet Analysis Employing Music Information Retrieval Techniques

2004 ◽  
Vol 92 (4) ◽  
pp. 712-729 ◽  
Author(s):  
B. Kostek
Keyword(s):  
Information Retrieval ◽  
Music Information Retrieval ◽  
Musical Instrument ◽  
Music Information ◽  
Musical Instrument Classification

Various Approaches in Musical Instrument Identification

2019 ◽  
Vol 10 (2) ◽  
pp. 1-7 ◽  
Author(s):  
Seema R. Chaudhary ◽  
Sangeeta N Kakarwal
Keyword(s):  
Neural Networks ◽  
Information Retrieval ◽  
Music Information Retrieval ◽  
Musical Instrument ◽  
Instrument Identification ◽  
Musical Instrument Identification ◽  
Music Information ◽  
Work Done ◽  
Audio Sample ◽  
Musical Instrument Classification

In the music information retrieval (MIR) field, it is highly desirable to know what instruments are used in an audio sample. Musical instrument classification is one of the sub domains of music information retrieval. Many researchers have presented different approaches for identifying western instruments and those approaches proved to be good for instrument identification. In this article, we have presented work done by the various authors to identify musical instrument using various approaches such sparse based representation, bio-inspired hierarchical model, joint modelling, Bayesian networks, neural networks, convolution neural networks, individual partials, clustering, and segmentation.

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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