tag :: music | BibSonomy

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1 PiJAMA: Piano Jazz With Automatic MIDI Annotations
https://almostimplemented.github.io/PiJAMA/
3 часов назад , @topel
mir
music
music_generation
mirmusicmusic_generation
(0)
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- Запись сообщества
- посмотреть историю записи
1RISM - Répertoire International des Sources Musicales
Répertoire International des Sources Musicales – The organization, founded in Paris in 1952, is the largest and only global operation that documents written musical sources.
9 дней назад , @tabularii
archives
manuscripts
music
printed_music
archivesmanuscriptsmusicprinted_music
(0)
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1Pluck
https://string.spiel.com/
11 дней назад , @bshanks
classic
classical
js
music
string
classicclassicaljsmusicstring
(0)
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- посмотреть историю записи
1Musicians Wanted - Search Local Musician Classifieds : BandMix.com
https://www.bandmix.com/
один месяц назад , @bshanks
music
music
(0)
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- Запись сообщества
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1BandMatch
https://bandmatch.app/
один месяц назад , @bshanks
music
music
(0)
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- Запись сообщества
- посмотреть историю записи

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2PiJAMA: Piano Jazz with Automatic MIDI Annotations
D. Edwards, S. Dixon, и E. Benetos. Transactions of the International Society for Music Information Retrieval, 6 (1): 89–102 (2023)
3 часов назад , @topel
mir
music
music_generation
mirmusicmusic_generation
(0)
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2Music Streaming Services: Understanding the drivers of customer purchase and intention to recommend these services
M. Lopes Barata. (2021)
2 месяцев назад , @andreagulam
industry
music
services
streaming
industrymusicservicesstreaming
(0)
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2Music Streaming Services: Understanding the drivers of customer purchase and intention to recommend these services
M. Lopes Barata. (2021)
2 месяцев назад , @sop2-ffzg
streaming
music
services
industry
from:andreagulam
streamingmusicservicesindustryfrom:andreagulam
(0)
копироватьудалитьдобавить публикацию в буфер
2What the digitalisation of music tells us about capitalism, culture and the power of the information technology sector
D. Hesmondhalgh, и L. Meier. (2017)
2 месяцев назад , @sop2-ffzg
music
culture
technology
from:andreagulam
musicculturetechnologyfrom:andreagulam
(0)
копироватьудалитьдобавить публикацию в буфер
2What the digitalisation of music tells us about capitalism, culture and the power of the information technology sector
D. Hesmondhalgh, и L. Meier. (2017)
2 месяцев назад , @andreagulam
culture
music
technology
culturemusictechnology
(0)
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1What the digitalisation of music tells us about capitalism, culture and the power of the information technology sector
D. Meier. (2017)
2 месяцев назад , @sop2-ffzg
music
culture
technology
from:andreagulam
musicculturetechnologyfrom:andreagulam
(0)
копироватьудалитьдобавить публикацию в буфер
2Musical Exploration via Streaming Services: The Norwegian Experience
Y. Kjus. (2016)
2 месяцев назад , @andreagulam
discovery
music
services
streaming
discoverymusicservicesstreaming
(0)
копироватьудалитьдобавить публикацию в буфер
2Musical Exploration via Streaming Services: The Norwegian Experience
Y. Kjus. (2016)
2 месяцев назад , @sop2-ffzg
streaming
music
services
discovery
from:andreagulam
streamingmusicservicesdiscoveryfrom:andreagulam
(0)
копироватьудалитьдобавить публикацию в буфер
4Effects of recommendations on the playlist creation behavior of users
I. Kamehkhosh, G. Bonnin, и D. Jannach. User Modeling and User-Adapted Interaction, 30 (2): 285--322 (01.04.2020)
2 месяцев назад , @sop2-ffzg
analysis
consumption
preferences
Mobile
Recommendation
platforms
Music
Automated
Playlists
Online
Choice
User
study
tools
recommendations
from:kamber
Spotify
Playlist
streaming
music
systems
Interactive
digitization
construction
apps
behavior
creation
analysisconsumptionpreferencesMobileRecommendationplatformsMusicAutomatedPlaylistsOnlineChoiceUserstudytoolsrecommendationsfrom:kamberSpotifyPlayliststreamingmusicsystemsInteractivedigitizationconstructionappsbehaviorcreation
(0)
копироватьудалитьдобавить публикацию в буфер
2From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation System
W. Assuncao, R. Prates, и L. Zaina. Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems, ACM, (октября 2023)
2 месяцев назад , @sop2-ffzg
User
engagement
Acceptance
habits
recommendation
playlists
Method
User-created
analysis
from:kamber
Inspection
experience
Technology
Accuracy
Contextual
Preferences
Listening
Intermediate
Music
Model
Cold-start
Semiotic
Track
UserengagementAcceptancehabitsrecommendationplaylistsMethodUser-createdanalysisfrom:kamberInspectionexperienceTechnologyAccuracyContextualPreferencesListeningIntermediateMusicModelCold-startSemioticTrack
(0)
копироватьудалитьдобавить публикацию в буфер
3LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis
M. Schedl, S. Brandl, O. Lesota, E. Parada-Cabaleiro, D. Penz, и N. Rekabsaz. ACM SIGIR Conference on Human Information Interaction and Retrieval, ACM, (марта 2022)
2 месяцев назад , @sop2-ffzg
listening
in
Collaborative
approaches
analysis
Fairness
classification
Content-based
Demographic
dataset
Music
Genre
information
User
records
data
Metadata
from:kamber
systems
Recommender
biases
Algorithmic
Last.fm
Style
recommender
Lyrics
listeninginCollaborativeapproachesanalysisFairnessclassificationContent-basedDemographicdatasetMusicGenreinformationUserrecordsdataMetadatafrom:kambersystemsRecommenderbiasesAlgorithmicLast.fmStylerecommenderLyrics
(0)
копироватьудалитьдобавить публикацию в буфер
2Multi-agent system application for music features extraction, meta-classification and context analysis
J. Perez-Marcos, D. Jimenez-Bravo, J. De Paz, G. Villarrubia Gonzalez, V. Lopez, и A. Gil. Knowledge and Information Systems, 62 (1): 401--422 (01.01.2020)
2 месяцев назад , @sop2-ffzg
Auto-classification
Social
Performance
Multi-label
analysis
intelligence
classification
Meta-classifiers
Single-label
Distributed
Emotional
Music
processing
Genre
information
Artificial
Twitter
recommendation
learning
networks
from:kamber
Machine
Multi-agent
systems
Contextual
Last.fm
evaluation
Multi-classification
Auto-classificationSocialPerformanceMulti-labelanalysisintelligenceclassificationMeta-classifiersSingle-labelDistributedEmotionalMusicprocessingGenreinformationArtificialTwitterrecommendationlearningnetworksfrom:kamberMachineMulti-agentsystemsContextualLast.fmevaluationMulti-classification
(0)
копироватьудалитьдобавить публикацию в буфер
2The Impact of Algorithmically Driven Recommendation Systems on Music Consumption and Production: A Literature Review
D. Hesmondhalgh, R. Campos Valverde, D. Kaye, и Z. Li. UK Centre for Data Ethics and Innovation Reports, (2023)
2 месяцев назад , @sop2-ffzg
music
systems
recommendation
algorithm
from:kamber
musicsystemsrecommendationalgorithmfrom:kamber
(0)
копироватьудалитьдобавить публикацию в буфер
3Understanding User-Curated Playlists on Spotify: A Machine Learning Approach
M. Pichl, E. Zangerle, и G. Specht. Int. J. Multimed. Data Eng. Manag., 8 (4): 44–59 (01.10.2017)
2 месяцев назад , @sop2-ffzg
Analysis
Learning
Content
Music
Retrieval
Data
Information
from:kamber
Machine
User-Generated
AnalysisLearningContentMusicRetrievalDataInformationfrom:kamberMachineUser-Generated
(0)
копироватьудалитьдобавить публикацию в буфер
4Effects of recommendations on the playlist creation behavior of users
I. Kamehkhosh, G. Bonnin, и D. Jannach. User Modeling and User-Adapted Interaction, 30 (2): 285--322 (01.04.2020)
2 месяцев назад , @kamber
Automated
Choice
Interactive
Mobile
Music
Online
Playlist
Playlists
Recommendation
Spotify
User
analysis
apps
behavior
construction
consumption
creation
digitization
music
platforms
preferences
recommendations
streaming
study
systems
tools
AutomatedChoiceInteractiveMobileMusicOnlinePlaylistPlaylistsRecommendationSpotifyUseranalysisappsbehaviorconstructionconsumptioncreationdigitizationmusicplatformspreferencesrecommendationsstreamingstudysystemstools
(0)
копироватьудалитьдобавить публикацию в буфер
2From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation System
W. Assuncao, R. Prates, и L. Zaina. Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems, ACM, (октября 2023)
2 месяцев назад , @kamber
Acceptance
Accuracy
Cold-start
Contextual
Inspection
Intermediate
Listening
Method
Model
Music
Preferences
Semiotic
Technology
Track
User
User-created
analysis
engagement
experience
habits
playlists
recommendation
AcceptanceAccuracyCold-startContextualInspectionIntermediateListeningMethodModelMusicPreferencesSemioticTechnologyTrackUserUser-createdanalysisengagementexperiencehabitsplaylistsrecommendation
(0)
копироватьудалитьдобавить публикацию в буфер
3LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis
M. Schedl, S. Brandl, O. Lesota, E. Parada-Cabaleiro, D. Penz, и N. Rekabsaz. ACM SIGIR Conference on Human Information Interaction and Retrieval, ACM, (марта 2022)
2 месяцев назад , @kamber
Algorithmic
Collaborative
Content-based
Demographic
Fairness
Genre
Last.fm
Lyrics
Metadata
Music
Recommender
Style
User
analysis
approaches
biases
classification
data
dataset
in
information
listening
recommender
records
systems
AlgorithmicCollaborativeContent-basedDemographicFairnessGenreLast.fmLyricsMetadataMusicRecommenderStyleUseranalysisapproachesbiasesclassificationdatadatasetininformationlisteningrecommenderrecordssystems
(0)
копироватьудалитьдобавить публикацию в буфер
2Multi-agent system application for music features extraction, meta-classification and context analysis
J. Perez-Marcos, D. Jimenez-Bravo, J. De Paz, G. Villarrubia Gonzalez, V. Lopez, и A. Gil. Knowledge and Information Systems, 62 (1): 401--422 (01.01.2020)
2 месяцев назад , @kamber
Artificial
Auto-classification
Contextual
Distributed
Emotional
Genre
Last.fm
Machine
Meta-classifiers
Multi-agent
Multi-classification
Multi-label
Music
Performance
Single-label
Social
Twitter
analysis
classification
evaluation
information
intelligence
learning
networks
processing
recommendation
systems
ArtificialAuto-classificationContextualDistributedEmotionalGenreLast.fmMachineMeta-classifiersMulti-agentMulti-classificationMulti-labelMusicPerformanceSingle-labelSocialTwitteranalysisclassificationevaluationinformationintelligencelearningnetworksprocessingrecommendationsystems
(0)
копироватьудалитьдобавить публикацию в буфер

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music как концепция всех пользователей

BibSonomy

закладки (спрятать)5653
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1 PiJAMA: Piano Jazz With Automatic MIDI Annotations

1RISM - Répertoire International des Sources Musicales

1Pluck

1Musicians Wanted - Search Local Musician Classifieds : BandMix.com

1BandMatch

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2PiJAMA: Piano Jazz with Automatic MIDI Annotations

2Music Streaming Services: Understanding the drivers of customer purchase and intention to recommend these services

2Music Streaming Services: Understanding the drivers of customer purchase and intention to recommend these services

2What the digitalisation of music tells us about capitalism, culture and the power of the information technology sector

2What the digitalisation of music tells us about capitalism, culture and the power of the information technology sector

1What the digitalisation of music tells us about capitalism, culture and the power of the information technology sector

2Musical Exploration via Streaming Services: The Norwegian Experience

2Musical Exploration via Streaming Services: The Norwegian Experience

4Effects of recommendations on the playlist creation behavior of users

2From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation System

3LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis

2Multi-agent system application for music features extraction, meta-classification and context analysis

2The Impact of Algorithmically Driven Recommendation Systems on Music Consumption and Production: A Literature Review

3Understanding User-Curated Playlists on Spotify: A Machine Learning Approach

4Effects of recommendations on the playlist creation behavior of users

2From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation System

3LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis

2Multi-agent system application for music features extraction, meta-classification and context analysis

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