MLP LYRICAL SYNTHESIS FOR PREDICTOR

MUSICAL EXPRESSIONS

2003 – 12 – 18

ECE 539 Introduction to :

Aritificial Neural Network

and Fuzzy Systems

Koji Yabumoto9017180622

MLP Lyrical Analysis

● % of Unique Words● # of Unique Words● Average Word Length● # of Lyrics● # of Characters

Input Feature Vectors:

C Application

● Traversal of directory in search of lyric data (*.lyr)

● Parsing and loading lyrics into proper data array structure.

● Filtering of data skewing characters.● Analysis to extract needed characteristics of

lyrics● Output into file with proper format for MLP

program.

MLP Development● Normalization of

Feature Vectors ● Optimal solution for #

of layers and # of neurons/layer.

● Compete Against Baseline Kmeans algorithm (~70%) Rate

● Try to achieve a Test Crate nearly as good as Train Crate

2 3 4 5 6 7 8 9 100

10

20

30

40

50

60

70

80

90

100

Crate Test,Train 2-Class MLP

Train Crate

Test Crate

Number of hidden layers

Cla

ssifi

catio

n (

%)

Modifications to Original Specification

● Study of data input feature vectors to determine correlation with classification.

● Changing the size of the ouput classification to improve performance.

● Study of different types of data's effectiveness.