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

3.  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 BeatRoot

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

5. Architecture of BeatRoot System  Onset Detection  Tempo Induction  Beat Tracking

6.  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 Onset Detection

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

8.  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) Tempo Induction

9.  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 Two steps

10.  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 Beat Tracking

11.  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 Beat Tracking

12. Solid circle: predicted beat times which correspond to onset Hollow circle: predicted beat times which don’t correspond to onset

13.  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 Beat Tracking

14. User Interface

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

17. [1] S. Dixon, "Evaluation of audio beat tracking system beatroot," Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007. [2] MIREX, Music Information Retrieval Evaluation eXchange Reference

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