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Evaluation of the Audio Beat Tracking System BeatRoot

Evaluation of the Audio Beat Tracking System BeatRoot. By Simon Dixon (JNMR 2007) Presentation by Yading Song Centre for Digital Music y ading.song@ eecs.qmul.ac.uk QMUL  ELE021  Music  &  Speech  Processing 27  February  2012. BeatRoot.

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Evaluation of the Audio Beat Tracking System BeatRoot

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  1. Evaluation of the Audio Beat Tracking System BeatRoot By Simon Dixon (JNMR 2007) Presentation by YadingSong Centre for Digital Music yading.song@eecs.qmul.ac.uk QMUL  ELE021  Music  &  Speech  Processing 27  February  2012

  2. BeatRoot • Identifying and synchronizing with the basic rhythmic pulse of a piece of music • An interactive beat tracking and metrical annotation system[1] • It uses a multiple agent architecture with different hypotheses • Rate • Placement of musical beats • Accurate tracking • Quick recovery from errors • Graceful degradation

  3. Steps • Tempo induction • Find the rate of beat • Beat tracking • Synchronize a quasi-regular pulse sequence with music

  4. Architecture of BeatRoot System • Onset Detection • Tempo Induction • Beat Tracking

  5. Onset Detection • Detection function • Spectral flux (used by Dixon) • Weighted phase deviation • Complex domain detection function • Spectral Flux • The square of the difference between the normalized magnitude of successive frames • How quickly the power spectrum of the a signal is changing • Peak-picking algorithm is used to find the local maxima • Onset detection function

  6. Spectral Flux Example of spectral flux “vivaldi.wav”, implemented in MIRtoolbox

  7. Tempo Induction • It calculates onsets times to compute clusters of inter-onset intervals (IOIs) • IOI = the time interval between any pair of onsets • Use clustering algorithm to find groups of similar IOIs • Represents various musical units (e.g. half notes)

  8. Two steps • 1. Clustering • Various of IOIs • Greedy algorithms • 2. Combining • Along with the No. of IOIs • To weight the clusters • A ranked list of tempo hypotheses is produced • Pass it to beat tracking sub-system

  9. Beat Tracking • It uses a multiple agent architecture to find sequence of events • Match various tempo hypotheses • Rate each sequence • Determine the most likely one • The music is processed sequentially from beginning to end • At any point the agents • Represent various hypotheses about the rate and timing of beat • Make prediction of next beats based on current states

  10. Beat Tracking • Each agent at the beginning • Is initialized with a tempo hypothesis • An onset time which is taken from the first few onsets, which defines the agent’s first beat time • Make prediction with given tempo and first beat time with a tolerance window • Onsets • In inner window – taken as actual beat time, stored and updated • In outer window – taken as possible beat times or not

  11. Beat Tracking Solid circle: predicted beat times which correspond to onset Hollow circle: predicted beat times which don’t correspond to onset

  12. Beat Tracking Each agent is equipped with an evaluation function which rates how well the predicted and actual beat correspond The agent with the highest score outputs sequence of beats as the solution to the beat tracking problem

  13. User Interface

  14. User Interface

  15. Evaluation Tempo Induction is correct in the most case Estimation of beat times are robust [2]

  16. Reference [1] S. Dixon, "Evaluation of audio beat tracking system beatroot," Journalof New Music Research, vol. 36, no. 1, pp. 39-51, 2007. [2] MIREX, Music Information Retrieval Evaluation eXchange

  17. Comments? Yading Song Centre for Digital Music yading.song@eecs.qmul.ac.uk

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