Music Mood Classifier

 Music Mood Classifier – IT and Computer Engineering Guide

1. Project Overview

Objective: Develop a machine learning model to classify the mood of a song based on audio features like tempo, key, and energy.
Scope: Enhance user experience in music applications by categorizing tracks into moods (e.g., happy, sad, energetic, calm).

2. Prerequisites

Knowledge: Understanding of classification algorithms, feature extraction techniques, and evaluation metrics.
Tools: Python, Librosa for audio processing, Pandas, NumPy, Scikit-learn, and Matplotlib.
Data: A dataset with audio features and corresponding mood labels (e.g., Spotify dataset).

3. Project Workflow

- Data Collection: Gather audio datasets with features like tempo, key, loudness, and mood labels.

- Feature Extraction: Use libraries like Librosa to extract relevant features from audio files.

- Data Preprocessing: Normalize data and handle missing or imbalanced samples.

- Model Training: Train classification models like Random Forest, SVM, or Neural Networks.

- Evaluation: Use metrics like accuracy, precision, recall, and F1 score to assess model performance.

- Deployment: Integrate the model into a music application or API for real-time mood classification.

4. Technical Implementation

Step 1: Import Libraries


import pandas as pd
import numpy as np
import librosa
import librosa.display
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix

Step 2: Load and Preprocess Data


# Load dataset
data = pd.read_csv('music_mood_data.csv')

# Normalize numerical features
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data[['tempo', 'energy', 'loudness']] = scaler.fit_transform(data[['tempo', 'energy', 'loudness']])

Step 3: Train-Test Split


# Define features and target
X = data[['tempo', 'energy', 'loudness', 'key']]
y = data['mood']

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 4: Train the Model


# Train a Random Forest Classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

Step 5: Evaluate the Model


# Make predictions
y_pred = model.predict(X_test)

# Evaluate performance
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

Step 6: Feature Importance


# Plot feature importance
importances = model.feature_importances_
plt.bar(X.columns, importances)
plt.xlabel('Features')
plt.ylabel('Importance')
plt.title('Feature Importance')
plt.show()

5. Results and Insights

Analyze the confusion matrix and classification report to assess model accuracy. Highlight features most influential in determining mood.

6. Challenges and Mitigation

Imbalanced Classes: Use techniques like SMOTE or class weights to handle imbalanced datasets.
Feature Extraction: Ensure accurate and consistent extraction of audio features.

7. Future Enhancements

Integrate more audio features like spectral contrast and MFCC.
Apply deep learning techniques (e.g., CNNs) for higher accuracy.

8. Conclusion

The Music Mood Classifier project demonstrates the application of machine learning to enhance the user experience in music applications by accurately categorizing songs into moods.