guitar chord detection
TRANSCRIPT
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Helin Wang & Tian Wang
4.21.2011
Guitar Chord Recognition
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Outline Problem Description
Different approaches
Mixture Component analysis
Principal Component Analysis
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Part 1: Problem
Description A guitar chord is a
collection of tones usually
sounded together at once.
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In time domain, the strength of sound decays as the time goes by.
C
B7
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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.
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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.
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Approach 1 & 2
Preprocessing
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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.
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Eigenface picture also holds the Locally Continuous property.
Importance of smoothing
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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).
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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.
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Approach 2: Principal Component Analysis
• Everything is same with eigenface analysis. Input is also
large dimension a vector.
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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.
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Result5 test data for each chord, 40 test data in all.
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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).
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CERLAB