Audio Ad Removal

Detecting and locating a 15-second sound clip within multiple MP3 files using scikit-learn (or related libraries) is an interesting problem that combines audio signal processing with machine learning. In this context, we need to extract features from the audio, train a model to identify those features, and then use the trained model to detect the target sound clip in new MP3 files.

To achieve this, you can follow a two-step process:

1. Feature extraction: Convert the MP3 files into a numerical format (like spectrograms or Mel-frequency cepstral coefficients - MFCCs) that can be fed into a machine learning model.
2. Model training and detection: Use machine learning algorithms (like classifiers) to detect the target sound clip in the input MP3 files.

Here is a basic workflow and Python code to help you get started using scikit-learn and librosa (a popular audio analysis library):

Step 1: Install the Required Libraries

You will need to install the following libraries:

• librosa: for audio processing.
• scikit-learn: for machine learning.
• numpy, matplotlib: for array manipulation and plotting.

pip install librosa scikit-learn numpy matplotlib

Step 2: Extract Features (MFCC or Spectrogram) from Audio Clips

We’ll extract the MFCCs (Mel-frequency cepstral coefficients) from the sound clips as they are a commonly used feature for speech and sound recognition tasks. These features can then be used by machine learning models.

Here’s how you can extract features from an audio file:

import librosa
import numpy as np
import os
import matplotlib.pyplot as plt

def extract_features(file_path, duration=15, sr=22050):
    # Load the audio file using librosa
    y, sr = librosa.load(file_path, sr=sr, duration=duration)
    
    # Extract MFCCs (Mel Frequency Cepstral Coefficients)
    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    
    # Average MFCCs across time (over columns) to get a fixed-length feature vector
    mfccs_mean = np.mean(mfccs, axis=1)
    
    return mfccs_mean

# Example usage
audio_file = "path_to_audio_file.mp3"
mfcc_features = extract_features(audio_file)
print(mfcc_features)

Step 3: Prepare Training Data

To train the model, you'll need labeled data: a set of audio clips and location labels where the target 15-second clip is located. If you have multiple MP3 files with annotations (i.e., start and end times of the target clip), you can use these to create training examples.

Here’s an example of how to prepare a dataset with features from multiple MP3 files:

def prepare_training_data(file_paths, target_clip_start, target_clip_end):
    features = []
    labels = []
    
    for file_path in file_paths:
        # Extract the features for each 15-second window
        mfcc_features = extract_features(file_path)
        
        # For simplicity, label each segment of the file (in real life, you would have actual annotations)
        if target_clip_start <= file_path <= target_clip_end:
            label = 1  # Target sound clip found
        else:
            label = 0  # No target clip
        
        features.append(mfcc_features)
        labels.append(label)
    
    return np.array(features), np.array(labels)

# Example usage
file_paths = ["audio1.mp3", "audio2.mp3", "audio3.mp3"]
target_clip_start = 30  # Target sound clip starts at second 30
target_clip_end = 45    # Target sound clip ends at second 45

X_train, y_train = prepare_training_data(file_paths, target_clip_start, target_clip_end)

Step 4: Train a Classifier

Once you've extracted features from your MP3 files and labeled the data, you can train a machine learning model using scikit-learn. A Random Forest classifier or Support Vector Machine (SVM) could work well for this kind of problem.

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

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

# Initialize and train a Random Forest classifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

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

# Evaluate the model
print(classification_report(y_test, y_pred))

Step 5: Detect the Target Sound Clip in New MP3 Files

Once you have a trained classifier, you can use it to detect the target clip in new MP3 files. You’ll apply the same feature extraction process to the new file and use the model to make predictions.

Here’s how to locate the sound clip within an MP3 file:

def detect_target_clip(file_path, clf, target_duration=15, sr=22050):
    # Slide a window of 15 seconds across the audio and predict
    y, sr = librosa.load(file_path, sr=sr)
    clip_length = target_duration * sr
    num_clips = len(y) // clip_length
    
    detected_times = []
    
    for i in range(num_clips):
        start_sample = i * clip_length
        end_sample = (i + 1) * clip_length
        clip = y[start_sample:end_sample]
        
        # Extract features from the current 15-second clip
        mfcc_features = np.mean(librosa.feature.mfcc(y=clip, sr=sr, n_mfcc=13), axis=1).reshape(1, -1)
        
        # Use the trained classifier to make a prediction (1: target sound clip detected, 0: not detected)
        prediction = clf.predict(mfcc_features)
        
        if prediction == 1:
            detected_times.append(i * target_duration)
    
    return detected_times

# Example usage
new_file = "new_audio.mp3"
detected_times = detect_target_clip(new_file, clf)
print("Target sound clip detected at times:", detected_times)

Notes:

1. Feature Extraction: We're using MFCCs to represent the audio features. You can experiment with other features like chroma, spectral contrast, or Mel-spectrograms.
2. Training Data: Ideally, you should have labeled data with known locations of the target sound clip in your MP3 files. If you don't have labeled data, you can create synthetic labels by marking where the 15-second clip appears in the training files.
3. Classifier Choice: You can experiment with other classifiers like Support Vector Machines (SVMs) or Gradient Boosting Machines (GBMs) depending on your data and needs.
4. Sliding Window: The detection step uses a sliding window approach to analyze the audio file in chunks (in this case, 15 seconds). You can adjust the window size based on the length of your target clip.

Step 6: Fine-tuning

You may need to fine-tune the model by adjusting hyperparameters, feature extraction methods, or the training data to improve the accuracy of detection.

This approach will get you started with sound clip detection using scikit-learn and librosa, and you can further refine the methodology based on your specific use case and data.