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Audio Tempo Extraction. Presenter: Simon de Leon Date: February 9, 2006 Course: MUMT611. Agenda. Introduction Algorithm Onset extraction Periodicity detection Temporal estimation of beat locations Examples Conclusion Discussion. Introduction. Tempo extraction is useful for

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Audio tempo extraction l.jpg

Audio Tempo Extraction

Presenter: Simon de Leon

Date: February 9, 2006

Course: MUMT611


Agenda l.jpg
Agenda

  • Introduction

  • Algorithm

    • Onset extraction

    • Periodicity detection

    • Temporal estimation of beat locations

  • Examples

  • Conclusion

  • Discussion


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Introduction

  • Tempo extraction is useful for

    • Automatic rhythm alignment

    • Beat-driven effects

    • Cut & paste operations in audio editing

  • Tempo extraction in general is mature for straightforward, rhythmic music (rock, rap, reggae, etc.)

  • The challenge is to be accurate across the widest range of genres


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Introduction

  • We will focus on the winning algorithm for MIREX 2005 [1]

  • The top algorithms belong to the class that performs the following:

    • Time-freq. analysis to determine beat onset

    • Pitch detection and autocorrelation techniques for periodicity estimation

  • Evaluation of algorithms is difficult due to different perceptions of rhythm


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Algorithm

  • Divided into three sections

    • Onset extraction

      • Where are the exact locations of the musical salient features?

    • Periodicity estimation

      • What is the tempo of the beats found?

    • Temporal estimation of beat locations

      • We found the onset locations in the spectral domain, but they are not all necessarily the beats


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Algorithm – Onset extraction

  • Idea is that the beat onsets correspond with

    • Note changes

    • Harmonic changes

    • Percussive events

  • Define spectral energy flux

    • Time derivative of the frequency component magnitudes

  • Technique of [1] assumes onsets correspond to the fastest change of frequency component magnitudes


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Algorithm – Onset extraction

  • Step 1: Take STFT of signal

  • Step 2: Take time derivative of frequency components (spectral energy flux)

    • a) Low-pass filter STFT magnitude

    • b) Apply logarithmic compression [2]

    • c) Pass through FIR filter differentiator [3]

  • Step 3: Use dynamic threshold and remove the smallest onset spectral energy flux “spikes” from previous step


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Algorithm – Onset extraction

  • Top left: Piano signal. Bottom left: STFT

  • Top right: Spectral energy flux. Bottom right: Detection function


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Algorithm – Onset extraction

  • Top left: Violin signal. Bottom left: STFT

  • Top right: Spectral energy flux. Bottom right: Detection function


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Algorithm – Periodicity Detection

  • Two techniques studied in [1]

    • Spectral product

    • Autocorrelation function

  • Assume tempo T is between 60bpm and 200bpm

  • Spectral product

    • Step 1) Take FFT of detection function

    • Step 2) For each frequency, multiply it by all of it’s integer multiples

    • Step 3) Largest product corresponds to frequency of periodicity


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Algorithm – Periodicity Detection

  • Autocorrelation function

    • Classical periodicity estimation, slightly outperforms spectral product method

    • It is the cross-correlation of a signal with itself

    • Three largest peaks of cross-correlation are analyzed for a multiplicity relationship


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Algorithm – Beat location

  • Given the tempo extracted from previous steps, we need to align the beat in phase

  • Step 1) Create pulse train q(t) with period Tderived from periodicity algorithm

  • Step 2) Find phase by cross-correlating q(t) with detection function, evaluating only at indices corresponding to detection function maximas

  • Step 3) For successive beats in an analysis window, simply add T and search for peak in detection function in vicinity

    • Repeat (2) to re-align phase if peak not found


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Examples

  • Let’s listen to some demos of the algorithm in action

    • Jazz – very good to good

    • Rock – very good

    • Classical – very bad to good

    • Soul – very good

    • Latin – satisfactory to good


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Conclusion

  • This algorithm represents the state-of-the-art in tempo extraction, the majority of the work focusing on onset detection

  • Problem areas

    • Long fading attacks and decays produce false onsets

    • Many instruments playing continuously with no stable regions produces too many false onsets

    • Cannot keep up when tempo varies quickly


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Conclusion

  • Results from [1] indicate roughly

    • 80-90% accuracy for classical, jazz, rock

    • 90-100% for latin, pop, reggae, soul, rap, techno

  • Results from [1] using MIREX database

    • 95% of the time gave correct tempo


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Discussion

  • Can evaluation methods be improved? How can we avoid the subjective nature of tempo perception?

  • Any suggestions on how we might improve the onset detection algorithm? How about the periodicity algorithm?


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References

[1] Alonso, Miguel, Bertrand David, and Gael Richard. 2004. Tempo and Beat Estimation of Musical Signals. Proceedings of the 5th International Conference on Music Information Retrieval.

[2] Klapuri, Anssi. 1999. Sound Onset Detection by Applying Psychoacoustic Knowledge. Proceedings of the IEEE International Conference of Acoustics, Speech and Signal Processing: 3089-3092.

[3] Proakis, John G., and Dimitris K. Manolakis. 1995. Digital Signal Processing: Principles, Algorithms and Applications. 3rd Ed. New York: Prentice Hall.


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