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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

Audio Tempo Extraction

Presenter: Simon de Leon

Date: February 9, 2006

Course: MUMT611

agenda
Agenda
  • Introduction
  • Algorithm
    • Onset extraction
    • Periodicity detection
    • Temporal estimation of beat locations
  • Examples
  • Conclusion
  • Discussion
introduction
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
introduction4
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
algorithm
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
algorithm onset extraction
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
algorithm onset extraction7
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
algorithm onset extraction8
Algorithm – Onset extraction
  • Top left: Piano signal. Bottom left: STFT
  • Top right: Spectral energy flux. Bottom right: Detection function
algorithm onset extraction9
Algorithm – Onset extraction
  • Top left: Violin signal. Bottom left: STFT
  • Top right: Spectral energy flux. Bottom right: Detection function
algorithm periodicity detection
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
algorithm periodicity detection11
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
algorithm beat location
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
examples
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
conclusion
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
conclusion15
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
discussion
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?
references
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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