Deep Learning Project for Playlist Recommendation based on a User's current session

Dependencies/ Uses:

Convolutional Neural Network (CNN) - Deep Learning for Feature Extraction.
- 3 ConvLayers and 2 FC-Layers.
Support Vector Machine (SVM) - Machine Learning for Classification.
- Linear, SVC.

User provides path to music folder/ library.
System retrieves all music files that end in .mp3 and stores in array.
System randomly seperates the music files array into training and testing arrays.
System "plays"/ displays individual songs to the user.
User labels each song as "liked" or "disliked".
System reads in each song, picks a 20s segment out 60s into the song and decodes .mp3 files to .wav.
System uses the .wav of each file to represent into corresponding Mel Spectrograms.
System scales the spectrograms by amplitude.
System proceeds to slice the spectrograms up into smaller pieces of the spectrogram for data augmentation purposes.
System defines the CNN parameters and proceeds to train the model using the training set.
System uses Theano to extract features from each spectrogram slice passed through the model for both sets.
System appends all the spectrogram slice's features together to represent that music file and reshapes the features accordingly.
System trains SVM model with extracted features and known labels from user input from the training set.
System iterates testing set and attempts to predict by classifying each testing song's features as "liked" or "disliked".