Evaluation of the Audio Beat Tracking System BeatRoot

Evaluation of the Audio Beat Tracking System BeatRootBy Simon Dixon (JNMR 2007)

Presentation by Yading SongCentre for Digital Musicyading.song@eecs.qmul.ac.uk

QMUL ELE021 Music & Speech Processing 27 February 20121Identifying and synchronizing with the basic rhythmic pulse of a piece of musicAn interactive beat tracking and metrical annotation system[1]It uses a multiple agent architecture with different hypothesesRate Placement of musical beatsAccurate trackingQuick recovery from errorsGraceful degradationBeatRoot

Tap the feet, clap their hands, dance in time with the music. 2Tempo inductionFind the rate of beat Beat trackingSynchronize a quasi-regular pulse sequence with music

StepsTwo subsystem. 3

Architecture of BeatRoot SystemOnset Detection

Tempo Induction

Beat Tracking

Detection function Spectral flux (used by Dixon)Weighted phase deviationComplex domain detection function Spectral FluxThe square of the difference between the normalized magnitude of successive framesHow quickly the power spectrum of the a signal is changing Peak-picking algorithm is used to find the local maxima Onset detection function

Onset DetectionSpectral Flux

Example of spectral flux vivaldi.wav, implemented in MIRtoolbox

Lots of tools.6It calculates onsets times to compute clusters of inter-onset intervals (IOIs)IOI = the time interval between any pair of onsetsUse clustering algorithm to find groups of similar IOIs Represents various musical units (e.g. half notes)

Tempo InductionHalf notes, or quarter notes. 7

1. ClusteringVarious of IOIsGreedy algorithms

2. CombiningAlong with the No. of IOIsTo weight the clustersA ranked list of tempo hypotheses is producedPass it to beat tracking sub-system Two steps25ms, is merged to combine the information about the clusters 8It uses a multiple agent architecture to find sequence of eventsMatch various tempo hypothesesRate each sequenceDetermine the most likely one The music is processed sequentially from beginning to endAt any point the agents Represent various hypotheses about the rate and timing of beatMake prediction of next beats based on current states

Beat TrackingThe most important and complex step in BeatRoot

9

Each agent at the beginningIs initialized with a tempo hypothesis An onset time which is taken from the first few onsets, which defines the agents first beat timeMake 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 TrackingTolerance window 10

Beat TrackingSolid circle: predicted beat times which correspond to onsetHollow circle: predicted beat times which dont correspond to onset6 onsets, A B C D E F Hollow and solid circle, evaluation function.rates how well is the prediction11Each 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 TrackingUser Interface

The audio data has been represented by the amplitude envelop and spectrogram. Onset, control pane. To properly use the interface, some training are needed. 13User Interface

Beat is the vertical line predicted beat, you can either modify or delete, you can drag it. . alternate metrical level, the at the top of diagram, you can see the inter-beat intervals in milliseconds, mark with detected onset.

14Tempo Induction is correct in the most case Estimation of beat times are robust [2]

EvaluationClassification of the musical style, metrical levelbla, bla, bla.

15[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 ReferenceYading SongCentre for Digital Musicyading.song@eecs.qmul.ac.uk

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