The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2001

AUTHORS

Gerhard Widmer

ABSTRACT

This paper reports on a long-term inter-disciplinary research project that aims at analysing the complex phenomenon of expressive music performance with machine learning and data mining methods. The goals and general research framework of the project are briefly explained, and then a number of challenges to machine learning (and also to computational music analysis) are discussed that arise from the complexity and multi-dimensionality of the musical phenomenon being studied. We also briefly report on first experiments that address some of these issues. More... »

PAGES

495-506

References to SciGraph publications

Book

TITLE

Principles of Data Mining and Knowledge Discovery

ISBN

978-3-540-42534-2
978-3-540-44794-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-44794-6_44

DOI

http://dx.doi.org/10.1007/3-540-44794-6_44

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027446487


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": "University of Vienna", 
          "id": "https://www.grid.ac/institutes/grid.10420.37", 
          "name": [
            "Dept. of Medical Cybernetics and Artificial Intelligence, University of Vienna, and Austrian Research Institute for Artificial Intelligence, Vienna"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Widmer", 
        "givenName": "Gerhard", 
        "id": "sg:person.013641401431.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013641401431.40"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1076/jnmr.31.1.37.8103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009581532"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/09298210008565464", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012185376"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/07494468900640451", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016881663"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-44795-4_47", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028554151", 
          "https://doi.org/10.1007/3-540-44795-4_47"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-44795-4_47", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028554151", 
          "https://doi.org/10.1007/3-540-44795-4_47"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1076/jnmr.30.1.39.7119", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032385808"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/09298219608570702", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036726484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1525/mp.2001.18.3.347", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043738974"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1007379606734", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051365551", 
          "https://doi.org/10.1023/a:1007379606734"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/09298219808570749", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051993963"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1121/1.2027087", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062306252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1121/1.402843", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062354132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1121/1.404425", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062355714"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2001", 
    "datePublishedReg": "2001-01-01", 
    "description": "This paper reports on a long-term inter-disciplinary research project that aims at analysing the complex phenomenon of expressive music performance with machine learning and data mining methods. The goals and general research framework of the project are briefly explained, and then a number of challenges to machine learning (and also to computational music analysis) are discussed that arise from the complexity and multi-dimensionality of the musical phenomenon being studied. We also briefly report on first experiments that address some of these issues.", 
    "editor": [
      {
        "familyName": "De Raedt", 
        "givenName": "Luc", 
        "type": "Person"
      }, 
      {
        "familyName": "Siebes", 
        "givenName": "Arno", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/3-540-44794-6_44", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-42534-2", 
        "978-3-540-44794-8"
      ], 
      "name": "Principles of Data Mining and Knowledge Discovery", 
      "type": "Book"
    }, 
    "name": "The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery", 
    "pagination": "495-506", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/3-540-44794-6_44"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "82bec31541929f32e1a05a6134cff564bcf67a4c4b7742ea6a70522232b32b66"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027446487"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/3-540-44794-6_44", 
      "https://app.dimensions.ai/details/publication/pub.1027446487"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T21:58", 
    "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_8693_00000260.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/3-540-44794-6_44"
  }
]
 

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.10.1007/3-540-44794-6_44'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-44794-6_44'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-44794-6_44'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-44794-6_44'


 

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

108 TRIPLES      23 PREDICATES      39 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/3-540-44794-6_44 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N2bee5c0aa1694ba3b25f38b6721d58ef
4 schema:citation sg:pub.10.1007/3-540-44795-4_47
5 sg:pub.10.1023/a:1007379606734
6 https://doi.org/10.1076/jnmr.30.1.39.7119
7 https://doi.org/10.1076/jnmr.31.1.37.8103
8 https://doi.org/10.1080/07494468900640451
9 https://doi.org/10.1080/09298210008565464
10 https://doi.org/10.1080/09298219608570702
11 https://doi.org/10.1080/09298219808570749
12 https://doi.org/10.1121/1.2027087
13 https://doi.org/10.1121/1.402843
14 https://doi.org/10.1121/1.404425
15 https://doi.org/10.1525/mp.2001.18.3.347
16 schema:datePublished 2001
17 schema:datePublishedReg 2001-01-01
18 schema:description This paper reports on a long-term inter-disciplinary research project that aims at analysing the complex phenomenon of expressive music performance with machine learning and data mining methods. The goals and general research framework of the project are briefly explained, and then a number of challenges to machine learning (and also to computational music analysis) are discussed that arise from the complexity and multi-dimensionality of the musical phenomenon being studied. We also briefly report on first experiments that address some of these issues.
19 schema:editor N7ba347378fcc49a5ae8ccae9ac9b1893
20 schema:genre chapter
21 schema:inLanguage en
22 schema:isAccessibleForFree true
23 schema:isPartOf N6b2166b190354abc9a16eb1a1a69eafd
24 schema:name The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery
25 schema:pagination 495-506
26 schema:productId N22aba5c05bf54fbaafbf83224b2d46e2
27 N7c44f8279be341048a982d4c2e06d889
28 Nf385856b765f47b09f45659670deff32
29 schema:publisher N9ffa77fedce04c97b969eb89b2e32f30
30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027446487
31 https://doi.org/10.1007/3-540-44794-6_44
32 schema:sdDatePublished 2019-04-15T21:58
33 schema:sdLicense https://scigraph.springernature.com/explorer/license/
34 schema:sdPublisher N19679f2f7fb847469081ba29cb99b247
35 schema:url http://link.springer.com/10.1007/3-540-44794-6_44
36 sgo:license sg:explorer/license/
37 sgo:sdDataset chapters
38 rdf:type schema:Chapter
39 N19679f2f7fb847469081ba29cb99b247 schema:name Springer Nature - SN SciGraph project
40 rdf:type schema:Organization
41 N22aba5c05bf54fbaafbf83224b2d46e2 schema:name readcube_id
42 schema:value 82bec31541929f32e1a05a6134cff564bcf67a4c4b7742ea6a70522232b32b66
43 rdf:type schema:PropertyValue
44 N2bee5c0aa1694ba3b25f38b6721d58ef rdf:first sg:person.013641401431.40
45 rdf:rest rdf:nil
46 N4fea103e754a49f88a6a849827ae5557 schema:familyName Siebes
47 schema:givenName Arno
48 rdf:type schema:Person
49 N621c880ee07a4a7f9356ee86fdd38c8a schema:familyName De Raedt
50 schema:givenName Luc
51 rdf:type schema:Person
52 N6b2166b190354abc9a16eb1a1a69eafd schema:isbn 978-3-540-42534-2
53 978-3-540-44794-8
54 schema:name Principles of Data Mining and Knowledge Discovery
55 rdf:type schema:Book
56 N7ba347378fcc49a5ae8ccae9ac9b1893 rdf:first N621c880ee07a4a7f9356ee86fdd38c8a
57 rdf:rest Nd23e9ac862ac47108e3d133e08e867a8
58 N7c44f8279be341048a982d4c2e06d889 schema:name dimensions_id
59 schema:value pub.1027446487
60 rdf:type schema:PropertyValue
61 N9ffa77fedce04c97b969eb89b2e32f30 schema:location Berlin, Heidelberg
62 schema:name Springer Berlin Heidelberg
63 rdf:type schema:Organisation
64 Nd23e9ac862ac47108e3d133e08e867a8 rdf:first N4fea103e754a49f88a6a849827ae5557
65 rdf:rest rdf:nil
66 Nf385856b765f47b09f45659670deff32 schema:name doi
67 schema:value 10.1007/3-540-44794-6_44
68 rdf:type schema:PropertyValue
69 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
70 schema:name Information and Computing Sciences
71 rdf:type schema:DefinedTerm
72 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
73 schema:name Artificial Intelligence and Image Processing
74 rdf:type schema:DefinedTerm
75 sg:person.013641401431.40 schema:affiliation https://www.grid.ac/institutes/grid.10420.37
76 schema:familyName Widmer
77 schema:givenName Gerhard
78 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013641401431.40
79 rdf:type schema:Person
80 sg:pub.10.1007/3-540-44795-4_47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028554151
81 https://doi.org/10.1007/3-540-44795-4_47
82 rdf:type schema:CreativeWork
83 sg:pub.10.1023/a:1007379606734 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051365551
84 https://doi.org/10.1023/a:1007379606734
85 rdf:type schema:CreativeWork
86 https://doi.org/10.1076/jnmr.30.1.39.7119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032385808
87 rdf:type schema:CreativeWork
88 https://doi.org/10.1076/jnmr.31.1.37.8103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009581532
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1080/07494468900640451 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016881663
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1080/09298210008565464 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012185376
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1080/09298219608570702 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036726484
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1080/09298219808570749 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051993963
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1121/1.2027087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062306252
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1121/1.402843 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062354132
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1121/1.404425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062355714
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1525/mp.2001.18.3.347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043738974
105 rdf:type schema:CreativeWork
106 https://www.grid.ac/institutes/grid.10420.37 schema:alternateName University of Vienna
107 schema:name Dept. of Medical Cybernetics and Artificial Intelligence, University of Vienna, and Austrian Research Institute for Artificial Intelligence, Vienna
108 rdf:type schema:Organization
 




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


...