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

PerformanceAudio 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 beatsComparing 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 leveltempo - beats per minuteInter-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.