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Lecture Music Processing Introduction Meinard Mller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Music Music Information Retrieval (MIR) MusicXML (Text) Sheet Music (Image) CD / MP3 (Audio) Dance /

  1. Lecture Music Processing Introduction Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de

  2. Music

  3. Music Information Retrieval (MIR) MusicXML (Text) Sheet Music (Image) CD / MP3 (Audio) Dance / Motion (Mocap) MIDI Music Singing / Voice (Audio) Music Film (Video) Music Literature (Text)

  4. Music Information Retrieval (MIR) Machine Learning Signal Processing Information Retrieval Music Musicology Library Sciences User Interfaces

  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 Bernstein (1962) Audio-ID Beethoven, Symphony No. 5 Beethoven, Symphony No. 5:  Bernstein (1962) Version-ID  Karajan (1982)  Gould (1992)  Beethoven, Symphony No. 9 Category-ID  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 Image Audio

  16. Music Synchronization: Image-Audio Image Audio

  17. How to make the data comparable? Image Audio

  18. How to make the data comparable? Image Processing: Optical Music Recognition Image Audio

  19. How to make the data comparable? Image Processing: Optical Music Recognition Image Audio Audio Processing: Fourier Analysis

  20. How to make the data comparable? Image Processing: Optical Music Recognition Image Audio 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 Structure Analysis Audio Features 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: 978-3-319-21944-8 Springer, 2015 Accompanying website: www.music-processing.de

  35. Book: Fundamentals of Music Processing Meinard Müller Fundamentals of Music Processing Audio, Analysis, Algorithms, Applications 483 p., 249 illus., hardcover ISBN: 978-3-319-21944-8 Springer, 2015 Accompanying website: www.music-processing.de

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