Purpose
I want to gauge the similarity between the songs for data cleaning and also maybe use this as a way to check if the music generated is more similar to the truth (the switching vocals version) than the baseline input (the original song).
What is MusicCNN?
It is a github repository based off the paper:
Exploring Data
Using the extractor, I plotted out the Taggram and got the tag likelihoods for a song (Justin Bieber – Love Yourself) and the switching vocals version of that song, to try out their model.
Comparison within a Song
Taggram Comparison
Some differences would be that
- there is no “opera” tag
- the “women” tag was detected
- The likelihood of tag detection is more concentrated at certain times.
Tags Likelihood Comparison
This is like the taggram averaged over time.
Differences:
- Decrease in “male” and “male vocals” tags likelihood
- “Opera” and “Quiet” tag likelihoods are eliminated.
- “female vocals”, “female” and “pop” are increased.
Comparison between songs
I’ll compare another original song against the switching vocals of a different song.
Taggram Comparison
This is pretty different from both taggrams of Justin Bieber’s Love Yourself
Tags Likelihood Comparison
Also pretty different, e.g. Tags likelihood for “techno”, “drums and “electronic” are higher for Maroon 5’s Maps than Justin Bieber’s Love Yourself.
Songs Mashup Comparison
Mashups are songs that are a mix of 2 or more songs. I want to see if there is a significant similarity in tag likelihood between songs contributing to the mashup and the mashup song.
Input Songs
Output Song
The mashup seems rather different from the songs making it up.
Actual Data Science
I’ll plot vectors of the tags and T-SNE it. Color of the points will correspond to songs grouped together.
#TODO
caffae 
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