Automatic Classification of Guitar Playing Modes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Raphael Foulon , Pierre Roy , François Pachet

ABSTRACT

When they improvise, musicians typically alternate between several playing modes on their instruments. Guitarists in particular, alternate between modes such as octave playing, mixed chords and bass, chord comping, solo melodies, walking bass, etc. Robust musical interactive systems call for a precise detection of these playing modes in real-time. In this context, the accuracy of mode classification is critical because it underlies the design of the whole interaction taking place. In this paper, we present an accurate and robust playing mode classifier for guitar audio signals. Our classifier distinguishes between three modes routinely used in jazz improvisation: bass, solo melodic improvisation, and chords. Our method uses a supervised classification technique applied to a large corpus of training data, recorded with different guitars (electric, jazz, nylon-strings, electro-acoustic). We detail our method and experimental results over various data sets. We show in particular that the performance of our classifier is comparable to that of a MIDI-based classifier. We describe the application of the classifier to live interactive musical systems and discuss the limitations and possible extensions of this approach. More... »

PAGES

58-71

References to SciGraph publications

  • 2013. Automatic String Detection for Bass Guitar and Electric Guitar in FROM SOUNDS TO MUSIC AND EMOTIONS
  • Book

    TITLE

    Sound, Music, and Motion

    ISBN

    978-3-319-12975-4
    978-3-319-12976-1

    Author Affiliations

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-12976-1_4

    DOI

    http://dx.doi.org/10.1007/978-3-319-12976-1_4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1006416099


    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": "Sony (France)", 
              "id": "https://www.grid.ac/institutes/grid.426401.5", 
              "name": [
                "Sony Computer Science Laboratory, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Foulon", 
            "givenName": "Raphael", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Sony (France)", 
              "id": "https://www.grid.ac/institutes/grid.426401.5", 
              "name": [
                "Sony Computer Science Laboratory, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Roy", 
            "givenName": "Pierre", 
            "id": "sg:person.015347035163.82", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015347035163.82"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Sony (France)", 
              "id": "https://www.grid.ac/institutes/grid.426401.5", 
              "name": [
                "Sony Computer Science Laboratory, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pachet", 
            "givenName": "Fran\u00e7ois", 
            "id": "sg:person.016502527757.13", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016502527757.13"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-41248-6_18", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016593182", 
              "https://doi.org/10.1007/978-3-642-41248-6_18"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/09298215.2010.512979", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028048811"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2470654.2481303", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042635025"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/comj.2008.32.3.72", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044750285"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tasl.2012.2191281", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061516917"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1121/1.1458024", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062266718"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icassp.2010.5495945", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093690753"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cbmi.2009.24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093813028"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/anziis.1994.396988", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094760510"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2014", 
        "datePublishedReg": "2014-01-01", 
        "description": "When they improvise, musicians typically alternate between several playing modes on their instruments. Guitarists in particular, alternate between modes such as octave playing, mixed chords and bass, chord comping, solo melodies, walking bass, etc. Robust musical interactive systems call for a precise detection of these playing modes in real-time. In this context, the accuracy of mode classification is critical because it underlies the design of the whole interaction taking place. In this paper, we present an accurate and robust playing mode classifier for guitar audio signals. Our classifier distinguishes between three modes routinely used in jazz improvisation: bass, solo melodic improvisation, and chords. Our method uses a supervised classification technique applied to a large corpus of training data, recorded with different guitars (electric, jazz, nylon-strings, electro-acoustic). We detail our method and experimental results over various data sets. We show in particular that the performance of our classifier is comparable to that of a MIDI-based classifier. We describe the application of the classifier to live interactive musical systems and discuss the limitations and possible extensions of this approach.", 
        "editor": [
          {
            "familyName": "Aramaki", 
            "givenName": "Mitsuko", 
            "type": "Person"
          }, 
          {
            "familyName": "Derrien", 
            "givenName": "Olivier", 
            "type": "Person"
          }, 
          {
            "familyName": "Kronland-Martinet", 
            "givenName": "Richard", 
            "type": "Person"
          }, 
          {
            "familyName": "Ystad", 
            "givenName": "S\u00f8lvi", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-12976-1_4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.3798882", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": {
          "isbn": [
            "978-3-319-12975-4", 
            "978-3-319-12976-1"
          ], 
          "name": "Sound, Music, and Motion", 
          "type": "Book"
        }, 
        "name": "Automatic Classification of Guitar Playing Modes", 
        "pagination": "58-71", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-12976-1_4"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "1e8fea7e1390cfe977c60a927bc5542ae1ebc00013a01a89dd4bdb827cc00c6a"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1006416099"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-12976-1_4", 
          "https://app.dimensions.ai/details/publication/pub.1006416099"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T11:32", 
        "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/v90/0000000001_0000000264/records_8660_00000247.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-3-319-12976-1_4"
      }
    ]
     

    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.10.1007/978-3-319-12976-1_4'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-12976-1_4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-12976-1_4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-12976-1_4'


     

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

    123 TRIPLES      23 PREDICATES      36 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-12976-1_4 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N40f0d0b10f394d7894a8bbf34f854301
    4 schema:citation sg:pub.10.1007/978-3-642-41248-6_18
    5 https://doi.org/10.1080/09298215.2010.512979
    6 https://doi.org/10.1109/anziis.1994.396988
    7 https://doi.org/10.1109/cbmi.2009.24
    8 https://doi.org/10.1109/icassp.2010.5495945
    9 https://doi.org/10.1109/tasl.2012.2191281
    10 https://doi.org/10.1121/1.1458024
    11 https://doi.org/10.1145/2470654.2481303
    12 https://doi.org/10.1162/comj.2008.32.3.72
    13 schema:datePublished 2014
    14 schema:datePublishedReg 2014-01-01
    15 schema:description When they improvise, musicians typically alternate between several playing modes on their instruments. Guitarists in particular, alternate between modes such as octave playing, mixed chords and bass, chord comping, solo melodies, walking bass, etc. Robust musical interactive systems call for a precise detection of these playing modes in real-time. In this context, the accuracy of mode classification is critical because it underlies the design of the whole interaction taking place. In this paper, we present an accurate and robust playing mode classifier for guitar audio signals. Our classifier distinguishes between three modes routinely used in jazz improvisation: bass, solo melodic improvisation, and chords. Our method uses a supervised classification technique applied to a large corpus of training data, recorded with different guitars (electric, jazz, nylon-strings, electro-acoustic). We detail our method and experimental results over various data sets. We show in particular that the performance of our classifier is comparable to that of a MIDI-based classifier. We describe the application of the classifier to live interactive musical systems and discuss the limitations and possible extensions of this approach.
    16 schema:editor Na024044d3091406e8e34eb843b971bc5
    17 schema:genre chapter
    18 schema:inLanguage en
    19 schema:isAccessibleForFree false
    20 schema:isPartOf N4737a1e4e16445b9a49a0edb7ff170c7
    21 schema:name Automatic Classification of Guitar Playing Modes
    22 schema:pagination 58-71
    23 schema:productId N59e7b3cc12fa4f60bd91ea297ca79c41
    24 N636ab90bf0b94c7b846f04b8bc76fd8e
    25 Ne0178a6b0704437a94b466ad70cb075d
    26 schema:publisher Nc057c574b39d45018859b6681fe9e7bb
    27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006416099
    28 https://doi.org/10.1007/978-3-319-12976-1_4
    29 schema:sdDatePublished 2019-04-15T11:32
    30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    31 schema:sdPublisher N46ee2fef1d2a4bd49346e761f93b935c
    32 schema:url http://link.springer.com/10.1007/978-3-319-12976-1_4
    33 sgo:license sg:explorer/license/
    34 sgo:sdDataset chapters
    35 rdf:type schema:Chapter
    36 N40f0d0b10f394d7894a8bbf34f854301 rdf:first Na464fbb6452543399ba5d58dc76184bc
    37 rdf:rest Nf975205c912f4a67bfbddacb995bd8fd
    38 N46ee2fef1d2a4bd49346e761f93b935c schema:name Springer Nature - SN SciGraph project
    39 rdf:type schema:Organization
    40 N4737a1e4e16445b9a49a0edb7ff170c7 schema:isbn 978-3-319-12975-4
    41 978-3-319-12976-1
    42 schema:name Sound, Music, and Motion
    43 rdf:type schema:Book
    44 N59e7b3cc12fa4f60bd91ea297ca79c41 schema:name readcube_id
    45 schema:value 1e8fea7e1390cfe977c60a927bc5542ae1ebc00013a01a89dd4bdb827cc00c6a
    46 rdf:type schema:PropertyValue
    47 N5a4837d60b8b439588dce35c4cd10e80 rdf:first N83f657c279d9423ba5838907a77dcd50
    48 rdf:rest rdf:nil
    49 N636ab90bf0b94c7b846f04b8bc76fd8e schema:name dimensions_id
    50 schema:value pub.1006416099
    51 rdf:type schema:PropertyValue
    52 N83f657c279d9423ba5838907a77dcd50 schema:familyName Ystad
    53 schema:givenName Sølvi
    54 rdf:type schema:Person
    55 N88ffd4330684462e99f9f114a3f03971 schema:familyName Aramaki
    56 schema:givenName Mitsuko
    57 rdf:type schema:Person
    58 Na024044d3091406e8e34eb843b971bc5 rdf:first N88ffd4330684462e99f9f114a3f03971
    59 rdf:rest Nf70b805a36284ff794de7f9d25759afc
    60 Na464fbb6452543399ba5d58dc76184bc schema:affiliation https://www.grid.ac/institutes/grid.426401.5
    61 schema:familyName Foulon
    62 schema:givenName Raphael
    63 rdf:type schema:Person
    64 Nc057c574b39d45018859b6681fe9e7bb schema:location Cham
    65 schema:name Springer International Publishing
    66 rdf:type schema:Organisation
    67 Nce590151963449918c3961caac1bbf18 schema:familyName Derrien
    68 schema:givenName Olivier
    69 rdf:type schema:Person
    70 Ne0178a6b0704437a94b466ad70cb075d schema:name doi
    71 schema:value 10.1007/978-3-319-12976-1_4
    72 rdf:type schema:PropertyValue
    73 Ne3d1ff4b5c8444b08c3f1816744c1521 schema:familyName Kronland-Martinet
    74 schema:givenName Richard
    75 rdf:type schema:Person
    76 Ne75b1cda671f4eff9488a08e6e96312d rdf:first Ne3d1ff4b5c8444b08c3f1816744c1521
    77 rdf:rest N5a4837d60b8b439588dce35c4cd10e80
    78 Neac71098f62b4e30bcedbfa73d84c5e8 rdf:first sg:person.016502527757.13
    79 rdf:rest rdf:nil
    80 Nf70b805a36284ff794de7f9d25759afc rdf:first Nce590151963449918c3961caac1bbf18
    81 rdf:rest Ne75b1cda671f4eff9488a08e6e96312d
    82 Nf975205c912f4a67bfbddacb995bd8fd rdf:first sg:person.015347035163.82
    83 rdf:rest Neac71098f62b4e30bcedbfa73d84c5e8
    84 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    85 schema:name Information and Computing Sciences
    86 rdf:type schema:DefinedTerm
    87 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    88 schema:name Artificial Intelligence and Image Processing
    89 rdf:type schema:DefinedTerm
    90 sg:grant.3798882 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-319-12976-1_4
    91 rdf:type schema:MonetaryGrant
    92 sg:person.015347035163.82 schema:affiliation https://www.grid.ac/institutes/grid.426401.5
    93 schema:familyName Roy
    94 schema:givenName Pierre
    95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015347035163.82
    96 rdf:type schema:Person
    97 sg:person.016502527757.13 schema:affiliation https://www.grid.ac/institutes/grid.426401.5
    98 schema:familyName Pachet
    99 schema:givenName François
    100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016502527757.13
    101 rdf:type schema:Person
    102 sg:pub.10.1007/978-3-642-41248-6_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016593182
    103 https://doi.org/10.1007/978-3-642-41248-6_18
    104 rdf:type schema:CreativeWork
    105 https://doi.org/10.1080/09298215.2010.512979 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028048811
    106 rdf:type schema:CreativeWork
    107 https://doi.org/10.1109/anziis.1994.396988 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094760510
    108 rdf:type schema:CreativeWork
    109 https://doi.org/10.1109/cbmi.2009.24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093813028
    110 rdf:type schema:CreativeWork
    111 https://doi.org/10.1109/icassp.2010.5495945 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093690753
    112 rdf:type schema:CreativeWork
    113 https://doi.org/10.1109/tasl.2012.2191281 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061516917
    114 rdf:type schema:CreativeWork
    115 https://doi.org/10.1121/1.1458024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062266718
    116 rdf:type schema:CreativeWork
    117 https://doi.org/10.1145/2470654.2481303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042635025
    118 rdf:type schema:CreativeWork
    119 https://doi.org/10.1162/comj.2008.32.3.72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044750285
    120 rdf:type schema:CreativeWork
    121 https://www.grid.ac/institutes/grid.426401.5 schema:alternateName Sony (France)
    122 schema:name Sony Computer Science Laboratory, Paris, France
    123 rdf:type schema:Organization
     




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


    ...