MLP Lyrical Analysis ● % of Unique Words ● # of Unique Words ● Average Word Length ● # of Lyrics ● # of Characters Input Feature Vectors:

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Presentation transcript:

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

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.