caratliff / ml-genre-assignment

This Jupyter Notebook-based project uses the Spotify API to gather audio data and employs clustering algorithms to enhance genre classification of tracks. The project includes exploratory data analysis and model performance evaluation to ensure accurate genre assignments.

Model-Based Approach to Music Genre Assignment

Musical genre classification is not an easy task. In this project, unsupervised and supervised machine learning techniques are utilizied to classify popular songs from Spotify based on audio features. This approach aims to replace human judgement with algorithmic decision-making to better assign genres to songs using musical features.

YouTube: Model Based Approach to Genre Assignment

Flow Chart

Guide

In the 10_clustering directory you'll find gaussian_mixture_clusters.ipynb and kmeans_cluster.ipynb, which are notebooks for the Unsupervised Machine Learning models. All of the accessory files for those notebooks are located in the same directory. 20_classification contains the notebook classification_code.ipynb for the Supervised Learning methods implemented. In the 30_docs you'll find both presentation slides on findings and a full report.

Getting started

Open your terminal and run the following lines in order:

1. git clone [e-mail]:rmratliffbrown/ml-genre-assignment.git

2. cd ml-genre-assignment

3. pip install -r requirements.txt

From here you'll be able to run any of notebooks in the repository without any trouble.

Data

The data was originally sourced utilizing Spotify's API, but can be easily accessed here:

https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-01-21/spotify_songs.csv

Data Dictionary

variable	class	description
track_id	string	Song unique ID
track_name	string	Song Name
track_artist	string	Song Artist
track_popularity	float	Song Popularity (0-100) where higher is better
track_album_id	string	Album unique ID
track_album_name	string	Song album name
track_album_release_date	string	Date when album released
playlist_name	string	Name of playlist
playlist_id	string	Playlist ID
playlist_genre	string	Playlist genre
playlist_subgenre	string	Playlist subgenre
danceability	float	Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
energy	float	Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
key	float	The estimated overall key of the track. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.
loudness	float	The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
mode	float	Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
speechiness	float	Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
acousticness	float	A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
instrumentalness	float	Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
liveness	float	Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
valence	float	A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
tempo	float	The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
duration_ms	float	Duration of song in milliseconds