Music is the art of arranging sound and noise together to create harmony, melody, rhythm, and expressive content. It is organized so that humans and sometimes other living organisms can express their current emotions with it.
We all have our own playlist, which we listen to while traveling, studying, dancing, etc.
In short, every emotion has a different genre. So here today, we will study how can we implement the task of genre classification using Machine Learning in Python.
Before starting the code, download the data from this link.
Let’s start with the code.
Import Libraries and Dataset
Firstly we need to import Libraries :
- Pandas: To import files/datasets.
- Matplotlib: To visualize the data frame.
- Numpy: To perform operations like scaling and correlation.
- Seaborn: To visualize the data frame.
- Librosa: To visualize the audio data. Install this library by “pip install librosa” command.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import librosa.display
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Now to import the data file run the below command.
music_data = pd.read_csv('file.csv')
music_data.head(5)
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Output :
Exploratory Data Analysis
Let’s find out the count of each music label.
music_data['label'].value_counts()
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Output:
blues 100 classical 100 country 100 disco 100 hiphop 100 jazz 100 metal 100 pop 100 reggae 100 rock 100
We can also analysis the sound waves of the audio using the Librosa library.
Let’s visualize few of them with the below code.
path = 'genres_original/blues/blues.00000.wav'
plt.figure(figsize=(14, 5))
x, sr = librosa.load(path)
librosa.display.waveplot(x, sr=sr)
id.Audio(path)
print("Blue")
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Output :
Blue
path = 'genres_original/metal/metal.00000.wav'
plt.figure(figsize=(14, 5))
x, sr = librosa.load(path)
librosa.display.waveplot(x, sr=sr,color='orange')
id.Audio(path)
print("Metal")
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Output :
Metal
path = 'genres_original/pop/pop.00000.wav'
plt.figure(figsize=(14, 5))
x, sr = librosa.load(path)
librosa.display.waveplot(x, sr=sr,color='purple')
id.Audio(path)
print("Pop")
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Output :
Pop
path = 'genres_original/hiphop/hiphop.00000.wav'
plt.figure(figsize=(14, 5))
x, sr = librosa.load(path)
librosa.display.waveplot(x, sr=sr,color='grey')
id.Audio(path)
print("HipHop")
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Output :
HipHop
import numpy as np
import seaborn as sns
# Computing the Correlation Matrix spike_cols = [col for col in data.columns if 'mean' in col]
# Set up the matplotlib figure f, ax = plt.subplots(figsize=(16, 11));
# Draw the heatmap with the mask and correct aspect ratio sns.heatmap(data[spike_cols].corr(), cmap='YlGn')
plt.title('Heatmap for MEAN variables', fontsize = 20)
plt.xticks(fontsize = 10)
plt.yticks(fontsize = 10);
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Output :
Data Preprocessing
Initially, we need to use LabelEncoder() to convert the labels into integer.
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
music_data['label'] = label_encoder.fit_transform(music_data['label'])
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As filename column is not a relevant, so we can drop it.
X = music_data.drop(['label','filename'],axis=1)
y = music_data['label']
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Now the data needs to be scaled, to make the model more stable and train fast.
cols = X.columns
minmax = preprocessing.MinMaxScaler()
np_scaled = minmax.fit_transform(X)
# new data frame with the new scaled data. X = pd.DataFrame(np_scaled, columns = cols)
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Model Training
Initially, split the model using train_test_split module.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.3,
random_state=111)
X_train.shape, X_test.shape, y_train.shape, y_test.shape |
We will be testing our datasets on below models :
- K-Neighbors Classifier : KNeighborsClassifier looks for topmost n_neighbors using different distance methods like Euclidean distance.
- Decision Tree Classifier : In Decision tree each node is trained by splitting the data is continuously according to a certain parameter.
- Random Forest : Random Forest Classifier fits a number of decision tree classifiers on many sub-samples of the dataset and then use the average to improve the results.
- Logistics Regression : Logistic Regression is a regression model that predicts the probability of a given data belongs to the particular category or not.
- Cat Boost : CatBoost implements decision trees and restricts the features split per level to one, which help in decreasing prediction time. It also handles categorical features effectively.
- Gradient Boost : In Gradient Boost an decision trees are implemented in a sequential manner which enhance the performance.
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import catboost as cb
from xgboost import XGBClassifier
rf = RandomForestClassifier(n_estimators=1000, max_depth=10, random_state=0)
cbc = cb.CatBoostClassifier(verbose=0, eval_metric='Accuracy', loss_function='MultiClass')
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05)
for clf in (rf, cbc, xgb):
clf.fit(X_train, y_train)
preds = clf.predict(X_test)
print(clf.__class__.__name__,accuracy_score(y_test, preds))
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Output :
RandomForestClassifier 0.78 CatBoostClassifier 0.8333333333333334 XGBClassifier 0.7933333333333333
Neural Network
Let’s evaluate the dataset with the simple Neural network.
import tensorflow.keras as keras
from tensorflow.keras import Sequential
from tensorflow.keras.layers import *
model = Sequential()
model.add(Flatten(input_shape=(58,)))
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization()) model.add(Dense(128, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(10, activation='softmax'))
model.summary() |
Output :
Compiling and fitting the model
# compile the model adam = keras.optimizers.Adam(lr=1e-4)
model.compile(optimizer=adam,
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
hist = model.fit(X_train, y_train,
validation_data = (X_test,y_test),
epochs = 100,
batch_size = 32)
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100 epochs will take some time.
Once done, then we can do evaluation.
Evaluation
Let’s check the test accuracy by below code.
test_error, test_accuracy = model.evaluate(X_test, y_test, verbose=1)
print(f"Test accuracy: {test_accuracy}")
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Output :
Test accuracy: 0.7566666603088379
Now we can evaluate the accuracy using line-plots.
fig, axs = plt.subplots(2,figsize=(10,10))
# accuracy axs[0].plot(hist.history["accuracy"], label="train")
axs[0].plot(hist.history["val_accuracy"], label="test")
axs[0].set_ylabel("Accuracy")
axs[0].legend()
axs[0].set_title("Accuracy")
# Error axs[1].plot(hist.history["loss"], label="train")
axs[1].plot(hist.history["val_loss"], label="test")
axs[1].set_ylabel("Error")
axs[1].legend()
axs[1].set_title("Error")
plt.show() |
Output :
Conclusion
Ensemble Learning and Neural nets has been proven the best way for classification of the genre with the accuracy of more than 80%