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António Pedro António Pedro OliveiraOliveira
DEI - FCTUCDEI - FCTUC21/12/200621/12/2006
Creative Generation ofCreative Generation ofAffective MusicAffective Music
05/02/23 António Pedro Oliveira 2
OutlineOutline
IntroductionIntroduction State of the ArtState of the Art ArchitectureArchitecture Work to be doneWork to be done
Experiments ExamplesExamples ConclusionConclusion
05/02/23 António Pedro Oliveira 3
IntroductionIntroduction
ProblemProblem No automatic method to detect emotions
related to music Music induce different emotions on
different listeners Context, user profile
HypothesisHypothesis Music is the main language of emotions
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IntroductionIntroduction
MotivationsMotivations Music is a ubiquitous media
Cinema, TV, radio, dance, transport, shopping centres, etc.
Emotional content can be influenced by specific musical features
ObjectivesObjectives Discover the isomorphic mapping between
musical features structure and emotions structure Generate music to induce emotions
Retrieve music features Sequence, compose music
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IntroductionIntroduction
MethodologyMethodology Music - Select, manipulate, sequence music
samples, adapting to intended emotion Audience - Compare intended and recognized
emotions using Affective Computing technology
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State of the ArtState of the Art
Music PsychologyMusic Psychology Emotions and Music Music perception and cognition Music theory
Computer MusicComputer Music Affective music generation Algorithmic composition
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State of the ArtState of the Art
Musical Signal ProcessingMusical Signal Processing Music Features Extraction
Melody Harmony Rhythm Instruments Dynamics
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State of the ArtState of the Art
Affective ComputingAffective Computing Psychophisiological techniques for emotion
recognition 2 Dimensional
Space
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System – stage 1System – stage 1
ArchitectureArchitecture
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System – stage 2System – stage 2
ArchitectureArchitecture
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System – stage 3System – stage 3
ArchitectureArchitecture
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System – stage 4System – stage 4
ArchitectureArchitecture
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Work to be done Work to be done
Research TopicsResearch Topics Music Psychology
Computational models to test isomorphic mappings between musical features structure and emotions structure
Computer Music Algorithms for Affective Music Composition Automatic Affective DJ Bridge the semantic gap in Music Features Extraction
Affective Computing Algorithms for emotion induction
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ExperimentsExperiments
Knowledge Base ConstructionKnowledge Base Construction N relations between musical features and emotions
Music Base LabellingMusic Base Labelling Music Features Extraction
Music GenerationMusic Generation Music composition, selection
Emotions RecognitionEmotions Recognition Affective Computing (Psychophisiological
techniques)
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ExamplesExamples
RhythmRhythm Tempo – X BPM
EmotionsEmotionsBob
Sinclair
Zero 7
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ExamplesExamples
MelodyMelody Melodic motion - pitch variation, notes density
EmotionsEmotionsSheryl
Crow
R.E.M.
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ExamplesExamples
HarmonyHarmony Consonance – music tension
EmotionsEmotionsDamien
RiceFaithless
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ExamplesExamples
InstrumentationInstrumentation Timbre – Spectral features
EmotionsEmotions
LouisArmstrong The Corrs
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ExamplesExamples
DynamicsDynamics Loudness – X Energy
EmotionsEmotionsGreen DayU2
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ConclusionConclusion
Scientific contributionsScientific contributions Knowledge base with relations between musical features
and emotions System to be used by musicians, psychologists, health
scientists
Engineering contributionsEngineering contributions Unifying functional system Autonomous DJ application
Artistic contributionsArtistic contributions Composition of new pieces of music
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ConclusionConclusion
Bring the knowledge of this Bring the knowledge of this cross-disciplinary approach cross-disciplinary approach into a computer systeminto a computer system
System advantagesSystem advantages Automatic Affective Music Generation
Computer games, movies, dance, etc. Flexibility to update:
Knowledge Base – useful for Music Psychology Music Base - useful for Computer Music