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Music Classification

Music Classification. Using Neural Networks Craig Dennis ECE 539. Problem and Motivation. People have hundreds of MP3s and other digital music files unclassified on their computer iTunes and other large digital music stores must classify thousands of files with many different genres

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Music Classification

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  1. Music Classification Using Neural Networks Craig Dennis ECE 539

  2. Problem and Motivation • People have hundreds of MP3s and other digital music files unclassified on their computer • iTunes and other large digital music stores must classify thousands of files with many different genres • Different genres sound different, so their frequency content should be different • Very difficult to choose frequency content • The goal is to classify music based on how it sounds using a neural network

  3. Data Collection • 3 Different Genres, 30 Samples Each • Classical (Beethoven, Mozart, etc.) • Pop (Coldplay, Madonna, etc.) • Classic Rock (Eric Clapton, Led Zeppelin, etc.) • Samples recorded at 44.1Khz and are the middle 5 seconds of the song

  4. Data Collection Continued • Frequency Content Analysis • Computed the Fast Fourier Transform of 50ms samples to get frequency content • Averaged the magnitude of 6 different frequency bands over 250ms samples • Total of 120 different frequency samples spanning both time and frequency • Also included length of song and tempo

  5. Sample Data • Pop Data • Song: The Killers – Mr. Brightside • Lots of low and high frequencies throughout entire 5 seconds • All instruments are playing, sample in a middle of a verse Magnitude Feature

  6. Sample Data • Classic Rock • Song: Cream – Sunshine Of Your Love • More low frequency content than high frequency content • Mostly during a guitar solo halfway through the song Magnitude Feature

  7. Sample Data • Classical • Song: Russian Dance from The Nutcracker • Short bursts of mid and high frequency content • Rather quiet part with some louder parts near the end of the sample Magnitude Feature

  8. Preliminary Results • Using K-Nearest-Neighbor with all features • Trained with 60 songs, test with 30 • Average classification rate using 3-way cross validation is 68.88% • Seems to classify Classical and Pop correctly however confuses Classic Rock as Pop • Multi-layer perceptron seems to choose all testing songs are from one genre for a classification rate of 33%

  9. Future Work • Feature reduction to reduce the 120 features to a more manageable 20 or 30 features • Try reduced features on Multi-layer peceptron and other neural networks

  10. Further Improvement • Increase the number of song samples • Have more precise frequency bands, break the frequency spectrum in to more than 6 pieces • Have more “important” features from the frequency bands, very hard to find

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