Music Processing Introduction Meinard Müller
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1 Lecture Music Processing Introduction Meinard Müller International Audio Laboratories Erlangen
2 Music
3 Music Information Retrieval (MIR) Sheet Music (Image) CD / MP3 (Audio) MusicXML (Text) Dance / Motion (Mocap) Music MIDI Singing / Voice (Audio) Music Film (Video) Music Literature (Text)
4 Music Information Retrieval (MIR) Signal Processing Musicology Music User Interfaces Machine Learning Information Retrieval Library Sciences
5 Piano Roll Representation
6 Player Piano (1900)
7 Piano Roll Representation (MIDI) J.S. Bach, C-Major Fuge (Well Tempered Piano, BWV 846) Time Pitch
8 Piano Roll Representation (MIDI) Query: Goal: Find all occurrences of the query
9 Piano Roll Representation (MIDI) Query: Goal: Find all occurrences of the query Matches:
10 Music Retrieval Database Query Hit Audio-ID Version-ID Category-ID Bernstein (1962) Beethoven, Symphony No. 5 Beethoven, Symphony No. 5: Bernstein (1962) Karajan (1982) Gould (1992) Beethoven, Symphony No. 9 Beethoven, Symphony No. 3 Haydn Symphony No. 94
11 Music Synchronization: Audio-Audio Beethoven s Fifth
12 Music Synchronization: Audio-Audio Beethoven s Fifth Orchester (Karajan) Piano (Scherbakov) Time (seconds)
13 Music Synchronization: Audio-Audio Beethoven s Fifth Orchester (Karajan) Piano (Scherbakov) Time (seconds)
14 Application: Interpretation Switcher
15 Music Synchronization: Image-Audio Audio Image
16 Music Synchronization: Image-Audio Audio Image
17 How to make the data comparable? Audio Image
18 How to make the data comparable? Image Processing: Optical Music Recognition Audio Image
19 How to make the data comparable? Image Processing: Optical Music Recognition Audio Image Audio Processing: Fourier Analysis
20 How to make the data comparable? Image Processing: Optical Music Recognition Audio Image Audio Processing: Fourier Analysis
21 Application: Score Viewer
22 Music Structure Analysis Example: Brahms Hungarian Dance No. 5 (Ormandy) Time (seconds)
23 Music Structure Analysis Example: Brahms Hungarian Dance No. 5 (Ormandy) Time (seconds)
24 Music Structure Analysis Example: Brahms Hungarian Dance No. 5 (Ormandy) A1 A2 B1 B2 C A3 B3 B4 Time (seconds)
25 Tempo Estimation and Beat Tracking Basic task: Tapping the foot when listening to music Example: Queen Another One Bites The Dust Time (seconds)
26 Tempo Estimation and Beat Tracking Basic task: Tapping the foot when listening to music Example: Queen Another One Bites The Dust Time (seconds)
27 Tempo Estimation and Beat Tracking Light effects Music recommendation DJ Audio editing
28 Why is Music Processing Challenging? Example: Chopin, Mazurka Op. 63 No. 3
29 Why is Music Processing Challenging? Example: Chopin, Mazurka Op. 63 No. 3 Waveform Amplitude Time (seconds)
30 Why is Music Processing Challenging? Example: Chopin, Mazurka Op. 63 No. 3 Waveform / Spectrogram Frequency (Hz) Time (seconds)
31 Why is Music Processing Challenging? Example: Chopin, Mazurka Op. 63 No. 3 Waveform / Spectrogram Performance Tempo Dynamics Note deviations Sustain pedal
32 Why is Music Processing Challenging? Example: Chopin, Mazurka Op. 63 No. 3 Waveform / Spectrogram Performance Tempo Dynamics Note deviations Sustain pedal Polyphony Main Melody Additional melody line Accompaniment
33 Music Processing Music Synchronization Fourier Transform Audio Features Structure Analysis Tempo and Beat Tracking Audio Decomposition Audio Identification
34 Book: Fundamentals of Music Processing Meinard Müller Fundamentals of Music Processing Audio, Analysis, Algorithms, Applications 483 p., 249 illus., hardcover ISBN: Springer, 2015 Accompanying website:
35 Book: Fundamentals of Music Processing Meinard Müller Fundamentals of Music Processing Audio, Analysis, Algorithms, Applications 483 p., 249 illus., hardcover ISBN: Springer, 2015 Accompanying website:
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