Using Scikit-learn for Music Information Retrieval: Python Algorithms for MIR – Steve Tjoa

Music Information Retrieval (MIR) is a field that involves the extraction of useful information from music signals. This tutorial will cover the basics of MIR using the Scikit-learn library in Python.

Scikit-learn is a powerful machine learning library in Python that provides tools for building and evaluating machine learning models. It also provides various algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.

In this tutorial, we will focus on using Scikit-learn for MIR tasks such as genre classification, mood detection, and instrument recognition.

Steps to follow:

1. Install Scikit-learn:
   Before you start using Scikit-learn, you need to install it. You can install Scikit-learn using pip by running the following command in your terminal:

pip install scikit-learn

1. Loading the data:
   The first step in any MIR task is to load the music data. You can use libraries such as Librosa to load audio files as numpy arrays. For this tutorial, we will use a dataset of music clips with corresponding genre labels.

import numpy as np
import pandas as pd

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

# Extract features from the audio files
X = []
for file in data['filename']:
    audio, sr = librosa.load(file)
    feature = extract_features(audio, sr)
    X.append(feature)

X = np.array(X)
y = data['genre']

1. Extracting features:
   The next step is to extract features from the audio files. Features are numerical representations of the audio signals that can be used for training machine learning models. There are various features that can be extracted from audio signals, such as mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features.

import librosa.feature

def extract_features(audio, sr):
    # Extract MFCC
    mfcc = librosa.feature.mfcc(y=audio, sr=sr)

    return mfcc.flatten()

1. Splitting the data:
   Before training the machine learning model, it is important to split the data into training and testing sets. This allows you to evaluate the model’s performance on unseen data.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

1. Training the model:
   Now that the data is preprocessed, you can train a machine learning model using Scikit-learn. For this tutorial, we will use a Support Vector Machine (SVM) classifier.

from sklearn.svm import SVC

# Create and train the SVM classifier
clf = SVC()
clf.fit(X_train, y_train)

1. Evaluating the model:
   Once the model is trained, you can evaluate its performance on the test set using metrics such as accuracy, precision, and recall.

from sklearn.metrics import accuracy_score

# Make predictions on the test set
y_pred = clf.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

1. Tuning hyperparameters:
   In some cases, you may want to tune the hyperparameters of the model to improve its performance. Scikit-learn provides tools such as GridSearchCV for this purpose.

from sklearn.model_selection import GridSearchCV

# Define the hyperparameters to tune
param_grid = {
    'C': [0.1, 1, 10],
    'kernel': ['linear', 'rbf'],
}

# Create a grid search object
grid_search = GridSearchCV(clf, param_grid)
grid_search.fit(X_train, y_train)

# Print the best hyperparameters
print(grid_search.best_params_)

Conclusion:
In this tutorial, we covered the basics of MIR using the Scikit-learn library in Python. We discussed how to load and preprocess music data, extract features, train a machine learning model, evaluate the model’s performance, and tune hyperparameters. With this knowledge, you can start building your own MIR applications using Scikit-learn.