A collection of scripts for visualizing the Arab Mashriq collection of the NYU Abu Dhabi Library and the Eisenberg collection
mfcc_t-SNE.ipynb: Compute MFCC from audio, reduce dimension to 2 with t-SNE, and plot
chromagram_t-SNE.ipynb: Compute chromagram from audio, reduce dimension to 3 and 2 with t-SNE, and plot
pca_t-SNE.ipynb: Compute mel spectrogram from audio, do PCA using different number of components, reduce dimension to 2, and plot alongside intensity
autoencoder_t_SNE: Compute mel spectrogram from audio, take bottleneck of autoencoder, reduce dimension to 2, and plot alongside intensity
compute_features.py: Compute, save, and plot STFT, chroma, and MFCC from audio
startAD_demo.ipynb: Independent similarity axis traversal visualization of MFCC and chroma from audio for startAD demo
features_VR.py: Compute selected features from audio, cross-refence song title with unique tag, and output csv with coordinates for VR