Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-03

AUTHORS

Nadine Hajj, Maurice Filo, Mariette Awad

ABSTRACT

With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features—which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers. More... »

PAGES

55-65

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40747-017-0052-x

DOI

http://dx.doi.org/10.1007/s40747-017-0052-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091104001


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "American University of Beirut", 
          "id": "https://www.grid.ac/institutes/grid.22903.3a", 
          "name": [
            "Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hajj", 
        "givenName": "Nadine", 
        "id": "sg:person.014473150463.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014473150463.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California, Santa Barbara", 
          "id": "https://www.grid.ac/institutes/grid.133342.4", 
          "name": [
            "Department of Mechanical Engineering, University of California Santa Barbara, 93117, Santa Barbara, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Filo", 
        "givenName": "Maurice", 
        "id": "sg:person.015307733265.20", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015307733265.20"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "American University of Beirut", 
          "id": "https://www.grid.ac/institutes/grid.22903.3a", 
          "name": [
            "Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Awad", 
        "givenName": "Mariette", 
        "id": "sg:person.015342313641.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015342313641.45"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1080/0929821042000317840", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001920230"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-38628-2_88", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002795671", 
          "https://doi.org/10.1007/978-3-642-38628-2_88"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.procs.2015.07.310", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008728793"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1878003.1878016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013699397"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1961295.1950385", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014568124"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2000064.2000066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020667311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-12242-2_42", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031181568", 
          "https://doi.org/10.1007/978-3-642-12242-2_42"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-12242-2_42", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031181568", 
          "https://doi.org/10.1007/978-3-642-12242-2_42"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/763612", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034207275"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/319463.319470", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036713367"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-20520-0_34", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042850832", 
          "https://doi.org/10.1007/978-3-642-20520-0_34"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-20520-0_34", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042850832", 
          "https://doi.org/10.1007/978-3-642-20520-0_34"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ijcnn.2013.6706753", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093215291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/aspaa.2005.1540233", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094187954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cimsvp.2009.4925643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094488874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wdm.2001.990162", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095609230"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-03", 
    "datePublishedReg": "2018-03-01", 
    "description": "With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features\u2014which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s40747-017-0052-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1136144", 
        "issn": [
          "2199-4536", 
          "2198-6053"
        ], 
        "name": "Complex & Intelligent Systems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms", 
    "pagination": "55-65", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7f0cf012d01935db7cc5ec4f8d38553f963e56aefae05e5f32f95d43e037f970"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s40747-017-0052-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1091104001"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s40747-017-0052-x", 
      "https://app.dimensions.ai/details/publication/pub.1091104001"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:56", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000347_0000000347/records_89804_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs40747-017-0052-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40747-017-0052-x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40747-017-0052-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40747-017-0052-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40747-017-0052-x'


 

This table displays all metadata directly associated to this object as RDF triples.

123 TRIPLES      21 PREDICATES      41 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s40747-017-0052-x schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N61437106e19e4397ba61853ce629f5b9
4 schema:citation sg:pub.10.1007/978-3-642-12242-2_42
5 sg:pub.10.1007/978-3-642-20520-0_34
6 sg:pub.10.1007/978-3-642-38628-2_88
7 https://doi.org/10.1016/j.procs.2015.07.310
8 https://doi.org/10.1080/0929821042000317840
9 https://doi.org/10.1109/aspaa.2005.1540233
10 https://doi.org/10.1109/cimsvp.2009.4925643
11 https://doi.org/10.1109/ijcnn.2013.6706753
12 https://doi.org/10.1109/wdm.2001.990162
13 https://doi.org/10.1145/1878003.1878016
14 https://doi.org/10.1145/1961295.1950385
15 https://doi.org/10.1145/2000064.2000066
16 https://doi.org/10.1145/319463.319470
17 https://doi.org/10.2307/763612
18 schema:datePublished 2018-03
19 schema:datePublishedReg 2018-03-01
20 schema:description With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features—which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers.
21 schema:genre research_article
22 schema:inLanguage en
23 schema:isAccessibleForFree true
24 schema:isPartOf N67886bb8cd924ed1adb1c4d54a281e2c
25 N88f12620cfe749d795df681f01fa3e6e
26 sg:journal.1136144
27 schema:name Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
28 schema:pagination 55-65
29 schema:productId N1bc491eb3147420585098ac3d6e4d148
30 N40b5e5786f804910b3ccd29ef1dd227f
31 N6737dbc9e19a4355acb28c2cc9989390
32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091104001
33 https://doi.org/10.1007/s40747-017-0052-x
34 schema:sdDatePublished 2019-04-11T09:56
35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
36 schema:sdPublisher N4787ca01ca4f40eba337915b502ff5be
37 schema:url https://link.springer.com/10.1007%2Fs40747-017-0052-x
38 sgo:license sg:explorer/license/
39 sgo:sdDataset articles
40 rdf:type schema:ScholarlyArticle
41 N1bc491eb3147420585098ac3d6e4d148 schema:name dimensions_id
42 schema:value pub.1091104001
43 rdf:type schema:PropertyValue
44 N1c9c4c0f38224d118b46c5707dd06a6f rdf:first sg:person.015342313641.45
45 rdf:rest rdf:nil
46 N40b5e5786f804910b3ccd29ef1dd227f schema:name doi
47 schema:value 10.1007/s40747-017-0052-x
48 rdf:type schema:PropertyValue
49 N4787ca01ca4f40eba337915b502ff5be schema:name Springer Nature - SN SciGraph project
50 rdf:type schema:Organization
51 N61437106e19e4397ba61853ce629f5b9 rdf:first sg:person.014473150463.63
52 rdf:rest Nabeef72fa9784b82a561fa432e3ee4b8
53 N6737dbc9e19a4355acb28c2cc9989390 schema:name readcube_id
54 schema:value 7f0cf012d01935db7cc5ec4f8d38553f963e56aefae05e5f32f95d43e037f970
55 rdf:type schema:PropertyValue
56 N67886bb8cd924ed1adb1c4d54a281e2c schema:issueNumber 1
57 rdf:type schema:PublicationIssue
58 N88f12620cfe749d795df681f01fa3e6e schema:volumeNumber 4
59 rdf:type schema:PublicationVolume
60 Nabeef72fa9784b82a561fa432e3ee4b8 rdf:first sg:person.015307733265.20
61 rdf:rest N1c9c4c0f38224d118b46c5707dd06a6f
62 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
63 schema:name Information and Computing Sciences
64 rdf:type schema:DefinedTerm
65 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
66 schema:name Artificial Intelligence and Image Processing
67 rdf:type schema:DefinedTerm
68 sg:journal.1136144 schema:issn 2198-6053
69 2199-4536
70 schema:name Complex & Intelligent Systems
71 rdf:type schema:Periodical
72 sg:person.014473150463.63 schema:affiliation https://www.grid.ac/institutes/grid.22903.3a
73 schema:familyName Hajj
74 schema:givenName Nadine
75 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014473150463.63
76 rdf:type schema:Person
77 sg:person.015307733265.20 schema:affiliation https://www.grid.ac/institutes/grid.133342.4
78 schema:familyName Filo
79 schema:givenName Maurice
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015307733265.20
81 rdf:type schema:Person
82 sg:person.015342313641.45 schema:affiliation https://www.grid.ac/institutes/grid.22903.3a
83 schema:familyName Awad
84 schema:givenName Mariette
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015342313641.45
86 rdf:type schema:Person
87 sg:pub.10.1007/978-3-642-12242-2_42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031181568
88 https://doi.org/10.1007/978-3-642-12242-2_42
89 rdf:type schema:CreativeWork
90 sg:pub.10.1007/978-3-642-20520-0_34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042850832
91 https://doi.org/10.1007/978-3-642-20520-0_34
92 rdf:type schema:CreativeWork
93 sg:pub.10.1007/978-3-642-38628-2_88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002795671
94 https://doi.org/10.1007/978-3-642-38628-2_88
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1016/j.procs.2015.07.310 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008728793
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1080/0929821042000317840 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001920230
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1109/aspaa.2005.1540233 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094187954
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1109/cimsvp.2009.4925643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094488874
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1109/ijcnn.2013.6706753 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093215291
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1109/wdm.2001.990162 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095609230
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1145/1878003.1878016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013699397
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1145/1961295.1950385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014568124
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1145/2000064.2000066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020667311
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1145/319463.319470 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036713367
115 rdf:type schema:CreativeWork
116 https://doi.org/10.2307/763612 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034207275
117 rdf:type schema:CreativeWork
118 https://www.grid.ac/institutes/grid.133342.4 schema:alternateName University of California, Santa Barbara
119 schema:name Department of Mechanical Engineering, University of California Santa Barbara, 93117, Santa Barbara, CA, USA
120 rdf:type schema:Organization
121 https://www.grid.ac/institutes/grid.22903.3a schema:alternateName American University of Beirut
122 schema:name Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
123 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...