Rhythmic Transcription of MIDI signals
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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.