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

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

Data Collection

3 Different Genres, 30 Samples EachClassical (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

Data Collection Continued

Frequency Content AnalysisComputed the Fast Fourier Transform of

50ms samples to get frequency contentAveraged the magnitude of 6 different

frequency bands over 250ms samplesTotal of 120 different frequency samples

spanning both time and frequencyAlso included length of song and tempo

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

Feature

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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

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Feature

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

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Feature

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%

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

Further Improvement

Increase the number of song samplesHave 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