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Sophomore Slumpware

Sophomore Slumpware. Predicting Album Sales with Artificial Neural Networks Matthew Wirtala ECE 539. Overview. Record sales have decreased ~30% over the past 4 years No consensus on why this is File-sharing? Inferior albums being released?. Overview.

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Sophomore Slumpware

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  1. Sophomore Slumpware Predicting Album Sales with Artificial Neural Networks Matthew Wirtala ECE 539

  2. Overview • Record sales have decreased ~30% over the past 4 years • No consensus on why this is • File-sharing? • Inferior albums being released?

  3. Overview • Perhaps album sales can be predicted with an MLP network • May show what factors determine how well an album will sell • Indicate which albums deserve a better marketing push

  4. Feature data • Critical acclaim • Review scores gathered from 4 sources • www.pitchforkmedia.com • www.allmusic.com • www.metacritic.com • Rolling Stone

  5. Feature data • Hype level • Amount of press coverage will lead to higher public awareness and possibly higher album sales • Previous album sales • Serve as barometer of how established an artist may be.

  6. Data labelling • Too difficult to predict exact album sales • Data labelled as one of three classes • Albums that sell fewer than 500,000 copies • Gold albums (500,000 – 1,000,000 copies) • Platinum albums ( > 1,000,000 copies sold)

  7. Data preprocessing • Data gathered for 60 albums • 20 from each class • Some from same artist falling into separate classes • Data randomized and split into three partitions • Feature vectors normalized to -5 - +5

  8. The Neural Network • Utilized Professor Hu’s standard bp.m algorithm • Trialed many different configurations • Optimal configuration • 2 hidden layers • 7 neurons in first layer, 8 in second • Learning rate = 0.267, momentum = 0.007 • Tested with 3-way cross validation

  9. Results • Highest classification rate 60% • Correctly classified class 1 and 2 albums with 80-90% accuracy • Could not separate class 2 albums • Class 2 featured albums with vectors similar to those of classes 1 and 3 • Sample confusion matrix: 4 0 2 2 0 5 1 0 6

  10. Future Improvements • Further analysis of feature vectors to determine possible differences in class 2 albums • Possible reduction of labelling to two classes (combine Gold and Platinum) • Classification does show that predictions can be made based on the features considered in this study

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