guitar chord detection

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