Music classification
Download
1 / 10

Music Classification - PowerPoint PPT Presentation


  • 411 Views
  • Updated On :

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Music Classification' - Sharon_Dale


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Music classification l.jpg

Music Classification

Using Neural Networks

Craig Dennis

ECE 539


Problem and motivation l.jpg
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 l.jpg
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


Data collection continued l.jpg
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


Sample data l.jpg
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


Sample data6 l.jpg
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


Sample data7 l.jpg
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


Preliminary results l.jpg
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 l.jpg
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 l.jpg
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