SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Classifying Music Genres Using Multi-Layer Perceptron: A 2009 Project Overview

rajah-stone avatar
rajah-stone
10 diapositives · 231 Views · Nov 30, 2014

This project by Chad Ostrowski and Curtis Reinking explores music genre classification using a Multi-Layer Perceptron (MLP) in MATLAB. We focused on classifying three genres: Post-Rock, Folk, and Hip-Hop. Using audio samples with a bit rate of 44100 Hz, we extracted features through the Fast Fourier Transform (FFT) and created an input layer to accommodate data from songs over six minutes long. Despite testing various network sizes, our initial setup with parameters of 30x100x100x3 did not yield the expected results. Join us as we navigate the challenges of machine learning in music appreciation.

#classifying music genres using multi-layer perceptron: a 2009 project overview #classifying music genres using multi-layer perceptron: a 2009 project overview ppt #powerpoint #presentation
Classifying Music Genres Using Multi-Layer Perceptron: A 2009 Project Overview - slide 1
01 / 10Classifying Music Genres Using Multi-Layer Perceptron: A 2009 Project Overview
1 / 17

Télécharger la présentation

Classifying Music Genres Using Multi-Layer Perceptron: A 2009 Project Overview
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  1. Teaching machines to appreciate music Classifying songs into three genres using a trusty Multi-Layer Perceptron A Project by Chad Ostrowski & Curtis Reinkingfor EE 456 (Neural Networks)in the spring of 2009

  2. Our genres-to-classify are Post-Rock, Folk, and Hip-Hop Can you guess which is which?

  3. Let’s feed songs-as-data-arrays into an MLP and let it do its thing! The sample bit rate of the type of music files MATLAB insists on is 44100Hz Good ol’ .wav (but the right kind of .wav (ACM Waveform, if you’re wondering). There are at least 4 kinds.) A lot of our songs are >6 minutes in length 6 minutes * 60 seconds/minute * 44100 data points/second = 15,876,000 data points so we’d have an input layer of that size. Hidden layers of…eh, 2e7. worse: this is a variable depending on the song.

  4. Let us chop it up. Let us extract features. folk hip hop Features, anyone?

  5. FFT (the Fast (or Discrete) Fourier Transform) ought to be a good way for a computer to learn about music it listens to Hip hop Folk

  6. It turns out double peaks are frightfully common. We mute them. Hip Hop Folk

  7. So all of our inputs are: Size of song Means of un-fft-ed clips (that’s four inputs) Means of fft-ed clips (that’s four more) The average number of “big” peaks Locations of the five tallest peaks in the fft of each clip (twenty inputs) 30 total inputs

  8. How to output? We recall that output vectors have no boundary problems and give greater accuracy than output scalars. 100 denotes folk 010 denotes post rock 001 denotes hip hop

  9. Drum-roll please!! We tested various network sizes, settling on 30x100x100x3. It crapped on us.

  10. (and that’s all we got) (so far)