guitar chord detection - preprocessing: fourier transform - detection: mixture component analysis v.s. principal component analysis. Trick: smooth the input signal.

1. Helin Wang & Tian Wang
4.21.2011
Guitar Chord Recognition

2. Outline
Problem Description
Different approaches
 Mixture Component analysis
 Principal Component Analysis

3. Part 1: Problem
Description A guitar chord is a
collection of tones usually
sounded together at once.

4. In time domain, the
strength of sound
decays as the time
goes by.
C
B7

5. In frequency domain, a chord
has its fundamental frequency
and integer multiple of
fundamental frequency.
Different musical instruments
has different weights of
fundamental frequency and
integer multiple frequencies.
Timbers are discriminated these
combinations.

6. Part 2: Different
Approaches
Data gathering and format
 Tool: Wavepad. Record a chord. And save it in WAV format in 2sec.
 Matlab read WAV file and generate a 1xn matrix, each number in the
matrix represents the sound’s strength in corresponding time.

7. Approach 1 & 2
Preprocessing

8. Why use…
-Band pass filter: guitar produce sound frequency
between ~15Hz - ~5000Hz.
-Guassian Smoothing: Required because we need
tolerance to the existance of guitar tuning error,
measurement error, computational error.

9. Eigenface picture also holds the Locally Continuous property.
Importance of smoothing

10. Approach 1: Mixture Component analysis
L*C = test
- L is formed by the 10000x1 chord feature vectors
of different chord.
We used 8 chords: A B7 C D E F G G7.
L = [A; B7; C; D; E; F; G; G7]; (10000x8).
- C is the coefficient matrix. (8x1)
- test is chord feature vector to be
tested(10000x1).
From equation:
Test is mixed by L with different percentage (c).

11. Approach 1: Mixture Component analysis
Least square solution:
C = inv(L'*L)*L'*test
We choose the biggest c_m.
Quality factor Q = c_m/sum(abs(C)) .
When Q > threshold, test data is
one of the chord in our database.

12. Approach 2: Principal Component Analysis
• Everything is same with eigenface analysis. Input is also
large dimension a vector.

13. Compare
Principal Component Analysis
With
Mixture Component Analysis:
In our content,
PCA does a better job in determining if test data is one of the
chord in our database.
MCA does better in recognition.

14. Result
5 test data for each chord, 40 test data in all.

15. Drawback
Database is 1 sample for 1 chord, high error.
Remedy
Use LDA or multi dimension Guassian pdf.
(their database can be n samples for 1 chord).

16. CERLAB