Clustering Music Genres with Machine Learning

Clustering is a machine learning technique to group data points characterized by specific features. Clustering music genres is a task of grouping music based on the similarities in their audio characteristics. If you want to learn how to perform clustering analysis on music genres, this article is for you. In this article, I will take you through the task of clustering music genres with machine learning using Python.

Clustering Music Genres (Problem Statement)

Every person has a different taste in music. We cannot identify what kind of music does a person likes by just knowing about their lifestyle, hobbies, or profession. So it is difficult for music streaming applications to recommend music to a person. But if we know what kind of songs a person listens to daily, we can find similarities in all the music files and recommend similar music to the person.

That is where the cluster analysis of music genres comes in. Here you are given a dataset of popular songs on Spotify, which contains artists and music names with all audio characteristics of each music. Your goal is to group music genres based on similarities in their audio characteristics.

You can download the dataset from here.

Clustering Music Genres using Python

I hope you have understood the problem statement mentioned above on clustering music genres with machine learning. Now let’s start with this task by importing the necessary Python libraries and the dataset:

import pandas as pd
import numpy as np
from sklearn import cluster

data = pd.read_csv("Spotify-2000.csv")
print(data.head())
   Index                   Title             Artist            Top Genre  \
0      1                 Sunrise        Norah Jones      adult standards   
1      2             Black Night        Deep Purple           album rock   
2      3          Clint Eastwood           Gorillaz  alternative hip hop   
3      4           The Pretender       Foo Fighters    alternative metal   
4      5  Waitin' On A Sunny Day  Bruce Springsteen         classic rock   

   Year  Beats Per Minute (BPM)  Energy  Danceability  Loudness (dB)  \
0  2004                     157      30            53            -14   
1  2000                     135      79            50            -11   
2  2001                     168      69            66             -9   
3  2007                     173      96            43             -4   
4  2002                     106      82            58             -5   

   Liveness  Valence Length (Duration)  Acousticness  Speechiness  Popularity  
0        11       68               201            94            3          71  
1        17       81               207            17            7          39  
2         7       52               341             2           17          69  
3         3       37               269             0            4          76  
4        10       87               256             1            3          59  

You can see all the columns of the dataset in the above output. It contains all the audio features of music that are enough to find similarities. Before moving forward, I will drop the index column, as it is of no use:

data = data.drop("Index", axis=1)

Now let’s have a look at the correlation between all the audio features in the dataset:

print(data.corr())
                            Year  Beats Per Minute (BPM)    Energy  \
Year                    1.000000                0.012570  0.147235   
Beats Per Minute (BPM)  0.012570                1.000000  0.156644   
Energy                  0.147235                0.156644  1.000000   
Danceability            0.077493               -0.140602  0.139616   
Loudness (dB)           0.343764                0.092927  0.735711   
Liveness                0.019017                0.016256  0.174118   
Valence                -0.166163                0.059653  0.405175   
Acousticness           -0.132946               -0.122472 -0.665156   
Speechiness             0.054097                0.085598  0.205865   
Popularity             -0.158962               -0.003181  0.103393   

                        Danceability  Loudness (dB)  Liveness   Valence  \
Year                        0.077493       0.343764  0.019017 -0.166163   
Beats Per Minute (BPM)     -0.140602       0.092927  0.016256  0.059653   
Energy                      0.139616       0.735711  0.174118  0.405175   
Danceability                1.000000       0.044235 -0.103063  0.514564   
Loudness (dB)               0.044235       1.000000  0.098257  0.147041   
Liveness                   -0.103063       0.098257  1.000000  0.050667   
Valence                     0.514564       0.147041  0.050667  1.000000   
Acousticness               -0.135769      -0.451635 -0.046206 -0.239729   
Speechiness                 0.125229       0.125090  0.092594  0.107102   
Popularity                  0.144344       0.165527 -0.111978  0.095911   

                        Acousticness  Speechiness  Popularity  
Year                       -0.132946     0.054097   -0.158962  
Beats Per Minute (BPM)     -0.122472     0.085598   -0.003181  
Energy                     -0.665156     0.205865    0.103393  
Danceability               -0.135769     0.125229    0.144344  
Loudness (dB)              -0.451635     0.125090    0.165527  
Liveness                   -0.046206     0.092594   -0.111978  
Valence                    -0.239729     0.107102    0.095911  
Acousticness                1.000000    -0.098256   -0.087604  
Speechiness                -0.098256     1.000000    0.111689  
Popularity                 -0.087604     0.111689    1.000000  

Clustering Analysis of Audio Features

Now I will use the K-means clustering algorithm to find the similarities between all the audio features. Then I will add clusters in the dataset based on the similarities we found. So let’s create a new dataset of all the audio characteristics and perform clustering analysis using the K-means clustering algorithm:

data2 = data[["Beats Per Minute (BPM)", "Loudness (dB)", 
              "Liveness", "Valence", "Acousticness", 
              "Speechiness"]]

from sklearn.preprocessing import MinMaxScaler
for i in data.columns:
    MinMaxScaler(i)
    
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=10)
clusters = kmeans.fit_predict(data2)

Now I will add the clusters as predicted by the K-means clustering algorithm to the original dataset:

data["Music Segments"] = clusters
MinMaxScaler(data["Music Segments"])
data["Music Segments"] = data["Music Segments"].map({1: "Cluster 1", 2: 
    "Cluster 2", 3: "Cluster 3", 4: "Cluster 4", 5: "Cluster 5", 
    6: "Cluster 6", 7: "Cluster 7", 8: "Cluster 8", 
    9: "Cluster 9", 10: "Cluster 10"})

Now let’s have a look at the dataset with clusters:

print(data.head())
                    Title             Artist            Top Genre  Year  \
0                 Sunrise        Norah Jones      adult standards  2004   
1             Black Night        Deep Purple           album rock  2000   
2          Clint Eastwood           Gorillaz  alternative hip hop  2001   
3           The Pretender       Foo Fighters    alternative metal  2007   
4  Waitin' On A Sunny Day  Bruce Springsteen         classic rock  2002   

   Beats Per Minute (BPM)  Energy  Danceability  Loudness (dB)  Liveness  \
0                     157      30            53            -14        11   
1                     135      79            50            -11        17   
2                     168      69            66             -9         7   
3                     173      96            43             -4         3   
4                     106      82            58             -5        10   

   Valence Length (Duration)  Acousticness  Speechiness  Popularity  \
0       68               201            94            3          71   
1       81               207            17            7          39   
2       52               341             2           17          69   
3       37               269             0            4          76   
4       87               256             1            3          59   

  Music Segments  
0      Cluster 1  
1      Cluster 6  
2      Cluster 2  
3      Cluster 2  
4      Cluster 3  

Now let’s visualize the clusters based on some of the audio features:

import plotly.graph_objects as go
PLOT = go.Figure()
for i in list(data["Music Segments"].unique()):
    

    PLOT.add_trace(go.Scatter3d(x = data[data["Music Segments"]== i]['Beats Per Minute (BPM)'],
                                y = data[data["Music Segments"] == i]['Energy'],
                                z = data[data["Music Segments"] == i]['Danceability'],                        
                                mode = 'markers',marker_size = 6, marker_line_width = 1,
                                name = str(i)))
PLOT.update_traces(hovertemplate='Beats Per Minute (BPM): %{x} <br>Energy: %{y} <br>Danceability: %{z}')

    
PLOT.update_layout(width = 800, height = 800, autosize = True, showlegend = True,
                   scene = dict(xaxis=dict(title = 'Beats Per Minute (BPM)', titlefont_color = 'black'),
                                yaxis=dict(title = 'Energy', titlefont_color = 'black'),
                                zaxis=dict(title = 'Danceability', titlefont_color = 'black')),
                   font = dict(family = "Gilroy", color  = 'black', size = 12))
Clustering Music Genres

So this is how we can perform cluster analysis of music genres with machine learning.

Summary

So this is how you can perform cluster analysis of music genres with machine learning using Python. Clustering music genres is a task of grouping music based on the similarities in their audio features. I hope you liked this article on clustering music genres with machine learning. Feel free to ask valuable questions in the comments section below.

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Aman Kharwal

Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder

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