Transcript Rhythmic Transcription of MIDI signals

Rhythmic Transcription of
MIDI Signals
Carmine Casciato
MUMT 611
Thursday, February 10, 2005
Uses of Rhythmic Transcription
Automatic scoring
Improvisation
Score following
Triggering of audio/visual components
Performance
Audio classification and retrieval
Genre classification
Ethnomusicology considerations
Sample database management
MIDI Signals
 Unidirectional message stream at 3.125KHz
 System Real Time Messages provide Timing
Tick message
 A simplification of acoustic signals
No noise, masking effects
 Easily retrieve note onsets, offsets, velocities,
pitches
 However, no knowledge of acoustic properties of
sound
Difficulties in Rhythmic Transcription
 Expressive performance vs mechanical
performance
 Inexact performance of notes
Syncopations
Silences
Grace notes
 Robustness of beat tracker
Can the tracker recover from incorrect beat induction?
 Real time implementation
Human Limits of Rhythmic Perception
Two note onsets are deemed synchronous
when played within 40ms of each other, 70
ms for > two notes
Piano and orchestral performances exhibit
note onset asynchronicity of 30-50ms
Note onset differences of 50ms to 2s give
rhythmic information
Evaluation Criteria for Beat Trackers
Informally - click track of reported beats
added to signal
Visually marking the reporting beats
Comparing reported vs known, correct
beats
Definitions (Dixon 2001)
Beat - “perceived pulses which are
approximately equally spaced and define
the rate at which notes in a piece are
played”
 meterical, score , performance level
tempo - beats per minute
Inter-onset Intervals (IOI) - time intervals
between note onsets
Approaches - Probabilistic Frameworks
Cemgil et al (2000) - Bayesian framework,
using a tempogram (wavelet) and a 10th
order Kalman Filter to estimate tempo,
which is a hidden state variable
Takeda et al (2002) - Hidden Markov
models for fluctuating note lengths and
note sequences, estimating both rhythms
and tempo
Raphael (2002) - tempo and rhythm
Approaches - Oscillators
 Period and phase that adjusts itself to
synchronize to IOI input
 Dannenberg and Allen (1990) - weighted IOIs
and credibility evaluation based on past input
 Meudic (2002) - real time implementation of
Dixon
Induce several beats and attempt to propagate them
through the signal (agents), then choose the best
 Pardo (2004) - Oscillator, compared to Cemgil
using same corpus
Pardo
Is a Kalman Filter (Cemgil) or oscillator
better for online tempo tracking?
Performance as time series of weights, W,
over T time steps
Weight of time step with no note onsets =
0, increased proportional to # of note
onsets
100ms is minimum IOI allowed, minimum
beat period
Pardo
 Uses weighted average of last 20 beat periods,
with one parameter varying degrees of
smoothing
 A correction parameter varies how far the period
and phase of the next predicted beat is changed
according to known information
 A window size parameter affects how many
periods may affect the current prediction
 Chose 5000 random values of these three
parameters, ran each triplet on 99 performances
of Cemgil corpora
Cemgil MIDI/Piano Corpora
Four pro jazz, four pro classical, three
amateur piano players
Yesterday and Michelle, fast, slow and
normal, on a Yamaha Diskclavier
Available at www.nici.kun.nl/mmm/
Pardo - Error Measurement
• After finding best parameters values for Michelle corpus,
applied same values to analysis of Yesterday corpus
• Compared to Cemgil using that paper’s defined error
metric, which takes into account both phase and period
errors, to come up with a score
Comparison of Approaches
• Oscillator somewhat better than tempogram alone,
• Somewhat worse than tempogram plus Kalman,
yet fall within standard deviation (bracketed numbers)
of Kalman scores
Other Considerations
Stylistic information
Training of tracker
Musical importance of note
Duration
Pitch
Velocity
Bibliography
 Allen, Paul and Roger Dannenberg. 1990. Tracking Musical Beats in Real Time. In
Proc. of the ICMC 1990, 140-143.
 Dixon, Simon. 2001. Automatic extraction of tempo and beat from expressive
performances. In Journal of New Music Research, 30,1, 39-58.
 Meudic, Benoit. 2002. A causal algorithm for beat-tracking. 2nd Conference
Understanding and Creating Music.
 Pardo, Bryan. 2004. Tempo tracking with a single oscillator. ISMIR 2004.
 Raphael, Christopher. 2002. A hybrid graphical model for rhythmic parsing. In
Artificial Intelligence, 137, 217-238.
 Takeda, Haruto, et al. 2002. Hidden Markov model for automatic transcription of MIDI
signals. In Proc. of Multimedia Signal Processing Workshop 2002.