Music Information Retrieval (MIR)
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1 Ringvorlesung Perspektiven der Informatik Sommersemester 2010 Meinard Müller Universität des Saarlandes und MPI Informatik Priv.-Doz. Dr. Meinard Müller 2007 Habilitation, Bonn 2007 MPI Informatik, Saarland Cluster of Excellence Music Signal Processing 5 PhD Students (2 Cluster, 3 DFG) Music Data Music Information Retrieval (MIR) Detection of semantic relations, e.g., harmonic, rhythmic, or motivic similarity Extraction of musical entities such as note events, instrumentation, or musical form Tools and methods for multimodal search, navigation, and interaction Piano Roll Representation Piano Roll Representation Player Piano (1900)
2 Piano Roll Representation (MIDI) J.S. Bach, C-Major Fuge (Well Tempered Piano, BWV 846) Piano Roll Representation (MIDI) Query: Goal: Find all occurrences of the query Time Pitch Piano Roll Representation (MIDI) Query: Audio Data Various interpretations Beethoven s Fifth Goal: Find all occurrences of the query Matches: Bernstein Karajan Scherbakov (piano) MIDI (piano) Memory Requirements Memory Requirements 1 Bit = 1: on 0: off 1 Byte = 8 Bits 1 Kilobyte (KB) = 1 Thousand Bytes 1 Megabyte (MB) = 1 Million Bytes 1 Gigabyte (GB) = 1 Billion Bytes 1 Terabyte (TB) = 1000 Billion Bytes MIDI files < 350 MB One audio CD 650 MB Two audio CDs > 1 Billion Bytes 1000 audio CDs Billions of Bytes
3 Music Synchronization: Audio-Audio Beethoven s Fifth Music Synchronization: Audio-Audio Beethoven s Fifth Karajan Karajan Scherbakov Scherbakov Synchronization: Karajan Scherbakov Music Synchronization: Audio-Audio Feature extraction: chroma features Music Synchronization: Audio-Audio Cost matrix 1 Karajan Scherbakov C C# D D# E F F# G C C# D D# E F F# G Karaja an G# A A# B G# A A# B Scherbakov Music Synchronization: Audio-Audio System: SyncPlayer/AudioSwitcher Cost-minimizing warping path Karaja an Scherbakov 0
4 Music Synchronization: MIDI-Audio Music Synchronization: MIDI-Audio MIDI = meta data Automated annotation Audio recording Sonification of annotations Music Synchronization: Scan-Audio Music Synchronization: Scan-Audio Scanned Sheet Music Correspondence Audio Recording Music Synchronization: Scan-Audio Music Synchronization: Scan-Audio Scanned Sheet Music Symbolic Note Events Scanned Sheet Music Symbolic Note Events OMR OMR Correspondence Correspondence Audio Recording Audio Recording
5 Music Synchronization: Scan-Audio System: SyncPlayer/SheetMusic Scanned Sheet Music Symbolic Note Events High Qualtity OMR Dirty but hidden Correspondence High Qualtity Audio Recording Music Synchronization: Lyrics-Audio Music Synchronization: Lyrics-Audio Difficult! Music Synchronization: Lyrics-Audio Music Synchronization: Lyrics-Audio Lyrics-Audio Lyrics-MIDI + MIDI-Audio
6 Given: CD recording Goal: Automatic extraction of the repetitive structure (or of the musical form) Example: Brahms Hungarian Dance No. 5 (Ormandy)
7 Similarity cluster Coarse Level Fine Level Coarse Level Fine Level What do different versions have in common? What are the characteristics of a specific version? What do different versions have in common? What are the characteristics of a specific version? What makes up a piece of music? What makes music come alive?
8 Coarse Level Fine Level Coarse Level Fine Level What do different versions have in common? What are the characteristics of a specific version? What do different versions have in common? What are the characteristics of a specific version? What makes up a piece of music? What makes music come alive? What makes up a piece of music? What makes music come alive? Identify despite of differences Identify the differences Identify despite of differences Identify the differences Example tasks: Audio Matching Cover Song Identification Example tasks: Performance Analysis Performance Analysis Performance Analysis 1. Capture nuances regarding tempo, dynamics, articulation, timbre, Performance: 2. Discover commonalities between different performances and derive general performance rules 3. Characterize the style of a specific musician (``Horowitz Factor ) Performance Analysis Performance Analysis Performance: Performance: Strategy: Compute score-audio synchronization and derive tempo curve Score (reference): Score (reference):
9 Performance Analysis Performance Analysis Performance: Performance: Tempo curve: Reference tempo Score (reference): What can be done if no reference is available? Relative Absolute Relative Absolute Given: Several versions Given: One version Given: Several versions Given: One version Comparison of extracted parameters Direct interpretation of extracted parameters Relative Absolute Relative Absolute Given: Several versions Given: One version Given: Several versions Given: One version Comparison of extracted parameters Direct interpretation of extracted parameters Comparison of extracted parameters Direct interpretation of extracted parameters Extraction errors have often no consequence on final result Extraction errors immediately become evident Extraction errors have often no consequence on final result Extraction errors immediately become evident Example tasks: Music Synchronization Genre Classification Example tasks: Music Transcription
10 Measure Tactus (beat) Tatum (temporal atom) Which temporal level? Local tempo deviations Sparse information (e.g., only note onsets available) Vague information (e.g., extracted note onsets corrupt) Performance Performance
11 Novelty Curve Novelty Curve Periodicity Analysis : Tempogram : Tempogram : Tempogram Motivic Similarity
12 Motivic Similarity Motivic Similarity Beethoven s Fifth (1st Mov.) Beethoven s Fifth (1st Mov.) Beethoven s Fifth (3rd Mov.) Motivic Similarity Multimodal Computing and Interaction Sheet Music (Image) MIDI CD / MP3 (Audio) Beethoven s Fifth (1st Mov.) Music Beethoven s Fifth (3rd Mov.) Beethoven s Appassionata Multimodal Computing and Interaction Sheet Music (Image) MIDI CD / MP3 (Audio) MusicXML (Text) Music Singing / Voice (Audio) Music Literature (Text) Music Film (Video) Dance / Motion (Mocap)
Music Information Retrieval (MIR)
Ringvorlesung Perspektiven der Informatik Wintersemester 2011/2012 Meinard Müller Universität des Saarlandes und MPI Informatik meinard@mpi-inf.mpg.de Priv.-Doz. Dr. Meinard Müller 2007 Habilitation, Bonn
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