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Lyrical Analysis and MLP Development: Exploring Unique Words and Optimal Data Structures

This project focuses on analyzing song lyrics to extract unique characteristics crucial for machine learning processes. It includes parsing lyric data, filtering skewed characters, and normalizing feature vectors for multi-layer perceptron (MLP) models. The goal is to compare MLP performance against baseline K-means algorithms, optimizing the number of layers and neurons for improved accuracy. Additionally, the study investigates the correlation between input feature vectors and classification effectiveness while refining output sizes for better model performance.

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Lyrical Analysis and MLP Development: Exploring Unique Words and Optimal Data Structures

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

  2. 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.

  3. 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

  4. 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.

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