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Mcgill university :: music technology :: mumt 611 >> beat tracking … WHAT IS BEAT TRACKING? 0 /17 What is beat tracking? Input audio … magic box … output tatum locations 1 /17 What is beat tracking? “…Estima(tion) of the possibly time-varying

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what is beat tracking
What is beat tracking?

Input audio

… magic box …

output tatum locations

1

/17

what is beat tracking4
What is beat tracking?

“…Estima(tion) of the possibly time-varying

tempo and locations of each beat. In Engineering

terms, this is the frequency and Phase

of a time-varying signal, the phase Of Which

is zero at a beat location (I.e., where

One would tap one’s foot).”

Hainsworth, 2006

2

/17

overview
Overview

>>

Motivation & definitions …

early work …

Discrete vs. continuous detection functions …

canonical work: scheirer …

autocorrelation versus comb filtering …

Exemplar Methods …

Persistent problems …

>>

>>

>>

>>

>>

>>

3

/17

motivation definitions
Motivation & definitions

>>

Auto accompaniment

Synchronization of 2 streams

Cd skipping recovery

Time-scaling algorithms

Tempo-synchronous effects/control

Database retrieval

similarity

>>

>>

>>

>>

>>

>>

4

/17

motivation definitions7
Motivation & definitions

Blimes divisions of musical timing

… metrical structure

… tempo variation

… timing deviations

… arrhythmic sections

3 hierarchal levels of metrical structure

… Tempo

… tactus

… tatum

Beat tracking / Tempo induction

>>

>>

>>

5

/17

approach overview
Approach overview

>>

>>

Rule based

… steedman (1977)

… parncutt (1994)

Autocorrelation

… Brown (1993)

… *Davies & Plumbley (2005)

Oscillating filters

… *Large (1994)

… *Scheirer (1998)

histogramming

… *seppanen (2001)

Multiple agent

… *goto (1995)

… Dixon (2001)

probabilistic

… hainsworth & macleod (2003)

… *klapuri (2003)

>>

>>

>>

>>

Red audio

Black symbolic

* causal

=

=

6

/17

=

early work
Early Work

>>

music perception and comp sci (1980’s)

Most early work with midi/symbolic data

Rule based

>>

>>

7

/17

early work10
Early Work

>>

steedman (1977)

large (1994)

Goto (1995)

Scheirer (1998)

M

I

D

I

>>

>>

A

U

D

I

O

>>

.

.

.

>>

8

/17

discrete df vs continuous df
Discrete df vs. continuous df

>>

Discrete detection function

… localized onset points, or IOI (inter-onset intervals)

… Suited for monophonic signals

… step 1: Created by various comparative time or time-freq techniques

… step 2: peak picking technique

Continuous detection function

… better for unknown onset densities

… same as step 1 above

… further processing required for important results

>>

9

/17

scheirer
Scheirer

.

Comb

filterbank

frequency

filterbank

Continuous

enveloping

.

.

Filt_1

Env_1

. . .

Filt_2

.

Filt_3

. . .

.

.

.

Input

audio

Sum

fltbks

.

. . .

Filt_4

.

. . .

Filt_5

. . .

Filt_6

Peak

pick

10

/17

acf vs comb filt
ACF vs Comb filt

.

Comb filters

*

Phase Alignment in 2nd step

Commonality not given directly

Meter estimation via decim. & sum

Less expensive

Automatic phase alignment

Possible tempi at multi & fracs

Meter estimation directly avail

>>

>>

>>

>>

>>

>>

>>

11

/17

slide14

winner

goto

freq

fltrbk

Multi agents

Sub_1

Input

audio

period

align

Sub_2

Dscrt

Onset

det

Cross

corr

Sub_3

Acf

.

.

.

Prior

kn0w

Sub_7

Prior knowledge: 1) frequent ioi is likely ibi

2) sounds likely to occur on beats

3) rhythmic pattern templates

4) chord templates for non-perc music

12

/17

klapuri

HMM

Bar

Bar

beat

beat

tatum

tatum

Sn-1

Sn

|

P(sn qn)

Observable variable conditioned by current state

=

klapuri

.

Period &

Align estim

freq

fltrbk

norm

Pwr env

.

.

Comb

fltrbk

Filt_01

Chan_1

Input

audio

Filt_02

.

Chan_2

.

.

.

.

Filt_03

.

Chan_3

.

.

.

Train

data

Chan_4

Filt_36

Training data rhythmic pattern templates

=

13

/17

davies plumbley
Davies & plumbley

.

.

.

.

periodicity

alignment

.

.

Comb

fltrbk

Comb

fltrbk

.

.

Cont

Detect

func

.

.

Input

audio

.

.

.

.

.

.

acf

.

.

Cont

dep

state

Gen

state

2 state model

14

/17

comparison
comparison

allowed

raw

Cml%

Tot%

Cml%

Tot%

23.8

38.9

29.8

48.5

scheirer

55.9

61.4

71.2

80.9

klapuri

Davies & plumbley

54.8

61.2

68.1

78.9

Raw Cml correct metrical level, continuity required

raw tot correct metrical level, continuity not required

Allowed cml 1/2 & 2x tempo allowed, continuity required

Allowed cml 1/2 & 2x tempo allowed, continuity not required

=

=

=

=

15

/17

persistent problems
Persistent problems

Areas for future work

>>

Periodicity switching

Half/double time

Alignment issues

Expressive timing

Non-percussive music

>>

>>

>>

>>

16

/17

conclusions
conclusions

much progress has been made through several approaches

Possible New methods of extracting periodicity and phase

we need to work on improving the robustness of calculations

>>

>>

>>

Thank you for your time!

17

/17

references
references

Davies, M.E.P., M. Plumbley. “Context-dependent beat tracking of musical

Audio,” IEEE Transactions on Audio, Speech and Language Processing, 15(3),

2007, pp. 1009-20.

Goto, M. “A study of real-time beat tracking for musical audio signals.”

PhD thesis, waseda university, 1998.

Hainsworth, s.w. “beat tracking and musical metre analysis,” in Signal processing

methods for music transcription, edited by a. Klapuri, and M. Davy, 101-129.

New york: Springer science and business media, 2006

Hainsworth, s.w. “techniques for the automated analysis of musical audio”,

PhD thesis, department of engineering, university of cambridge, 2004.

KLAPURI, A. “SIGNAL PROCESSING METHODS FOR THE AUTOMATIC TRANSCRIPTION OF

MUSIC” PHD THESIS, TAMPERE UNIVERSITY OF TECHNOLOGY, 2004.

Scheirer, e. “music listening systems”, PhD thesis Massachusetts institute of

Technology, 2000.