BTSMusical Acoustic Signals

A Real-time
Beat Tracking System
for Audio Signals

Compact DiscSounds of various kinds of instrumentsthat include drums
4Time signature :4Tempo : 65-185 M.M. almost constant

AP1000BTS
BTS

School of Science and Engineering, Waseda University

Parallel Computer

Masataka Goto
Beat Information

Yoichi Muraoka

Correspond to quarter notes

SDBD

Beat Time, Beat Type, Current Tempo
1995/09/04 ICMC 95

No.1

Beat Time

No.5

Beat TypeSWS

Transmitted in time to input musicW

2. Issues and Solutions

OutlineNo specific sound directly indicates the position of beats

1. Introduction

It is difficult to obtain precise onset times in audio

2. Issues and Solutions

Multiple onset-time finders detect various cues

3. System Description
Multiple interpretations of beats are possible
BTS should be able to recover from tracking errors

4. Experimental Results

Multiple agents maintain multiple hypotheses

5. Conclusion

2. Issues and Solutions

No.2

No.6

1. Introduction

3. System Description
Frequency Spectrum
Onset Components
Noise Components

What is Beat Tracking?
Track quarter notes

Multiple onset-time finders detect various cues
Multiple agents maintain multiple hypotheses

Compact Discfjust as people keep time to music by foot-tappingt

Why is Acoustic Beat-Tracking Important?

Musical
Audio Signals

Manager

Computational model of human music perception

Drums

Beat Information

AgentsOnset-time Finders

Multimedia applications
Video/Audio editing systems

Frequency
Analysis

A/D Conversion

Beat Prediction

Beat Information
Transmission

Live performances
Real-time computer graphics

3. System Description

1. Introduction

No.3

No.7

Frequency Analysis

Previous Systems

Frequency Spectrum
Onset Components
Noise Components

MIDI-input systems

ftClassical repertoire
AudioMusicalAudio Signals

ManagerDrums

Our System

Beat Information

AgentsOnset-time Finders

Real-time audio-input system

Fourteen onset-time finders
detect onset times in different frequency ranges
with different levels of sensitivity

Audio

Popular music

One drum-sound finder
detects onset times of two drum instruments(Bass drum and Snare drum)

1. Introduction

No.4

Multiple agents maintain multiple hypotheses

Compact DiscMIDI

Non-real-time audio-input systems

Complex sonic environment

Multiple onset-time finders detect various cues

No.8

Beat Prediction

Drum-sound finder[Bass Drum (BD)][Snare Drum (SD)]
Detected as
widely distributed noise components

Detected using
histogram of peak frequencies
frequency

Multiple agents maintain multiple hypotheses

Compact DiscfSD

frequencyt

7.5kHz

1kHz

Multiple onset-time finders detect various cues

Frequency Spectrum
Onset Components
Noise Components

Musical
Audio Signals

Beat Information

ManagerDrums

AgentsOnset-time Finders
BD

Twenty-eight agents
track beats according to different strategies

1.4kHz20Hz

populationtimePeak Histogram

Quantized Noise Components

Managergenerate beat information based on the most reliable hypothesis

The detected drums are used
only to label a beat time with its beat type

3. System Description

No.9

3. System Description

No.13

Beat Prediction

Multiple onset-time finders detect various cues

Frequency Spectrum
Onset Components
Noise Components

4. Experimental Results

Multiple agents maintain multiple hypotheses

Conditions

Compact Discf

44 popular songs performed by 31 artiststMusical
Audio Signals

Manager

Tempo: 67 - 185 M.M.

Beat Information

almost constant

Eight pre-registered drum patterns
Drums

AgentsOnset-time Finders

Results

Twenty-eight agentstrack beats according to different strategies

Correctly tracked beats in 42 out of 44 songs in real time
Mistakes in two songs

Managergenerate beat information based on the most reliable hypothesis

Half-tempo error
Wrong beat type

3. System Description

No.10

4. Experimental Results

No.14

Beat Prediction

Digital Art Application with BTS

How to make a hypothesis

Create real-time CG synchronized with music

1. Predict beat time

Display a virtual dancer "Cindy"

2. Evaluate reliability

whose motion is changed with beats in real timetime

predict by using autocorrelationand cross-correlationof onset times

onset times

Musical Acoustic Signals

predictedbeatsevaluate
how coincide

the next
beat time

Beat Information
BTSCD

extrapolated fromthe past two beat times

AP1000RMCP Animation ServerEthernet

3. Infer beat type

ParallelComputer
3. System Description

No.11

SGI Indigo2 Extreme

No.15

Beat Prediction

Currently detected drum pattern

5. Conclusion

predictedbeats

3. Infer beat typepredict
predictedbeatsSD

Track beats in audio signals in real time

BDthe nextbeat time
beat typeweakstrong

weakstrong

weakManage multiple beat-tracking agentsstrongSD | .
BD |O|O|.

||o |O|O.| o |

best-matchedpattern

Use drum patterns pre-registered as musical knowledge

Pre-registered drum patterns
Pattern 1
12(3)

(4)

12Matching

Robust enough to handle real-world audio signals

beat: 2
SD |X...|O...|BD |o...|X...|SDBD

Pattern 2
1SDBD

No.12

2

3

4

beat: 4
SD |X...|O...|X...|O...|BD |o...|x.Xx|X.O.|x...|3. System Description

5. Conclusion

No.16

Future Work
Upgrade our beat-tracking modelto understand music at a higher level
to deal with other musical genres
Application to various multimedia systems
for which beat tracking is useful

5. Conclusion

No.17