1. Rhythmic Transcription of MIDI Signals
Carmine Casciato
MUMT 611
Thursday, February 10, 2005

2. 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

3. 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

4. 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
(Dixon 2001)

5. 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
(Dixon 2001)

6. 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
(Dixon 2001)

7. Definitions
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
(Dixon 2001)

8. 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

9. 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

10. Pardo 2004 - Oscillatory Design
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

11. Pardo 2004
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

12. Cemgil MIDI/Piano Corpora
Four pro jazz, four pro classical, three amateur piano players
Yesterday and Michelle, fast, slow and normal, captured on a Yamaha Diskclavier
Available at

13. Pardo 2004 - 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

14. Comparison of Approaches
(Pardo 2004)
Oscillator somewhat better than tempogram alone,
Somewhat worse than tempogram plus Kalman,
yet fall within standard deviation (bracketed numbers)
of Kalman scores

15. Other Considerations Stylistic information Musical importance of note
Training of tracker
Musical importance of note
Duration
Pitch
Velocity

16. Bibliography
Allen, P., and R. Dannenberg Tracking musical beats in real time. In Proceedings of the International Computer Music Conference 1990: 140–3.
Dixon, S Automatic extraction of tempo and beat from expressive performances. Journal of New Music Research 30 (1): 39–58.
Meudic, B A causal algorithm for beat-tracking. In Proceedings of Conference on Understanding and Creating Music.
Pardo, B Tempo tracking with a single oscillator. In Proceedings of the International Conference on Music Information Retrieval 2004.
Raphael, C A hybrid graphical model for rhythmic parsing. Artificial Intelligence 137: 217–38.
Takeda, H., T. Nishimoto, and S. Sagayama Automatic rhythm transcription from multiphonic MIDI signals. In Proceedings of the International Conference on Music Information Retrieval 2003.