Evaluation of the Audio Beat Tracking System BeatRoot By Simon Dixon (JNMR 2007) Presentation by Yading Song Centre for Digital Music [email protected].

Evaluation of the Audio Beat Tracking System BeatRoot By Simon Dixon (JNMR 2007) Presentation by Yading Song Centre for Digital Music [email protected] QMUL ELE021 Music & Speech Processing 27 February 2012

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

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

Architecture of BeatRoot System Onset Detection Tempo Induction Beat Tracking

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

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

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

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

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

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 agents 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

Solid circle: predicted beat times which correspond to onset Hollow circle: predicted beat times which dont correspond to onset

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

User Interface

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

[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

Yading Song Centre for Digital Music [email protected] Comments?

Evaluation of the Audio Beat Tracking System BeatRoot By Simon Dixon (JNMR 2007) Presentation by Yading Song Centre for Digital Music [email protected].

Documents

tracking slide

rate of beat beat tracking

onset slide

evaluation slide

interactive beat tracking

beat tracking problem

timing of beat

mirtoolbox slide