Slide 1Slide 2Slide 3Slide 4Slide 5MLP LYRICAL SYNTHESIS FOR PREDICTORMUSICAL EXPRESSIONS2003 – 12 – 18ECE 539 Introduction to :Aritificial Neural Network and Fuzzy SystemsKoji Yabumoto9017180622MLP Lyrical Analysis●% of Unique Words●# of Unique Words●Average Word Length●# of Lyrics●# of CharactersInput 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 Crate2 3 4 5 6 7 8 9 100102030405060708090100Crate Test,Train 2-Class MLPTrain CrateTest CrateNumber of hidden layersClassification (%)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
View Full Document
Unlocking...