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Helin Wang & Tian Wang
4.21.2011
Guitar Chord Recognition
Outline Problem Description
Different approaches
Mixture Component analysis
Principal Component Analysis
Part 1: Problem
Description A guitar chord is a
collection of tones usually
sounded together at once.
In time domain, the strength of sound decays as the time goes by.
C
B7
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.
Part 2: Different
ApproachesData 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.
Approach 1 & 2
Preprocessing
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.
Eigenface picture also holds the Locally Continuous property.
Importance of smoothing
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).
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.
Approach 2: Principal Component Analysis
• Everything is same with eigenface analysis. Input is also
large dimension a vector.
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.
Result5 test data for each chord, 40 test data in all.
DrawbackDatabase is 1 sample for 1 chord, high error.
RemedyUse LDA or multi dimension Guassian pdf. (their database can be n samples for 1 chord).
CERLAB