This document outlines a plan to develop a real-time score tracking system using hidden Markov models. It discusses using score tracking to enable applications like automatic accompaniment and score viewing with automatic scrolling. The system will use audio input and compare it to a musical score to determine the current position. It aims to address the computational complexity of previous approaches through techniques like pruning and index searching. The development plan involves reproducing past systems, designing a new algorithm, implementing and evaluating a new system, and publishing results.

1. Real-Time Score Tracking
Using Hidden Markov Models
m5151154 Kousuke Sato
Computer Arts lab
Supervised by Satoshi Nishimura

2. Outline
 Score Tracking
 Applications
 Related Research(technology)
 Problem and Approach
 Current Status
 Future plan
 References

3. Score Tracking
 Score Tracking
It locates the performance position when we play
a musical instrument.
Player Score
B♭ A G
Playing notes : B♭→A → G Playing here

4. Applications of Score Tracking
 Learning tool for musical instruments
・ Automatic Accompaniment System
・ Score-Viewing System with Automatic Scrolling

5. Target users
 beginners of musical instruments
・ Automatic Accompaniment System
・ Score-Viewing System with
Automatic Scrolling
 Conductor
・ Score-Viewing System with
Automatic Scrolling

6. Related technology
 Pattern recognition
・DP Matching
The matching algorithm using matrix.
It is able to decide matching mathematically.
・HMM(Hidden Markov Model)
A state transition model
with probabilities.

7. System overview
Input
Audio(wave)
Musical score
signal
Fourier Generate
transform ideal spectrum
Spectrum Ideal
data(audio) Spectrum data
similarity
analysis
Hidden Markov Model
Output

8. Related research
 “A hybrid graphical model for aligning polyphonic
audio with musical scores”
C.Raphael, Proc.ISMIR, Barcelona,Spain,2004
・Search notes from current state, rear state and front state.
・Not match a sound refusing, it changes in the next state
State 1 State 2 State 3 ・・・ State N-1 State N
Provisional
state

9. Related research
 “Real-Time Musical Score Tracking System Using a
Performance Position Analysis Algorithm”
M.Yoshizawa, University of Aizu, March,2009.
・Search notes from all states
・The system can cope all cases
State 1 State 2 State 3 ・・・ State N-1 State N
Provisional
state

10. Problem
 Problem
It is too much computational complexity
when using past technology.
→ not usable in real time

11. Approaches for reducing
calculation
 Pruning
・Searching with precedence
→reduce a useless calculation in the Viterbi algorithm
 Index of the search
・Classifying score fragments by conditions
→ reduce useless calculation.

12. Current status
I read the papers of past researches
 Studied
the related technology
・DP Matching
・HMM(Hidden Markov Model)
and more…

13. Future plan
 Sep.2011~Oct.2011
I reproduce the system of a past researches.
Understanding the movement.
 Nov.2011~Mar.2012
Design new algorithm and design new system.
 Apr.2012~Sep.2012
Run a system and evaluate it.
Look for an improvement
 Oct.2012~
Writing research paper
Look for an improvement

14. References
 “A hybrid graphical model for aligning polyphonic audio
with musical scores”
C. Raphael, Proc. ISMIR, Barcelona,Spain,2004
 “Real-Time Musical Score Tracking System Using a
Performance Position Analysis Algorithm”
M. Yoshizawa, University of Aizu, March,2009.
 “A Performance Position Analysis Algorithm for
Automatic Accompaniment”
M. Maekawa, University of Aizu, March,2008
 “Score following using spectral analysis and hidden
markov models”
Orio N, Dechelle F, Proceedings of the ICMA,2001