Design (LLD) a music recognition system using audio fingerprinting - Machine Coding

Design (LLD) a music recognition system using audio fingerprinting - Machine Coding

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Features Required:

  1. Audio Fingerprint Generation: The system should be able to generate unique audio fingerprints for music tracks based on their audio content.

  2. Database Storage: The system should store the generated audio fingerprints along with metadata (e.g., song title, artist, album) in a database for efficient retrieval and matching.

  3. Query and Matching: Users should be able to submit audio samples to the system, which will be matched against the stored fingerprints to identify the corresponding music track.

  4. Audio Preprocessing: The system should perform preprocessing on the audio samples and fingerprints to enhance matching accuracy, such as noise reduction, filtering, and normalization.

  5. Fast Matching Algorithms: The system should employ fast matching algorithms, such as hashing or indexing techniques, to efficiently search and retrieve fingerprints from the database during matching.

  6. Robustness to Noise and Distortion: The system should be designed to handle audio samples with background noise, distortion, or low audio quality, ensuring accurate recognition in real-world scenarios.

  7. Scale and Performance: The system should be scalable to handle a large number of music tracks and perform matching in real-time or near real-time to provide a seamless user experience.

  8. Integration with Music Metadata: The system should integrate with external music metadata sources to provide additional information about recognized music tracks, such as album artwork, lyrics, or streaming links.

Design Patterns Involved or Used:

  1. Singleton Pattern: The Singleton pattern can be used to ensure that only one instance of the audio fingerprint generator and database manager is created and shared across the system.

  2. Factory Pattern: The Factory pattern can be used to create different objects, such as audio fingerprint generators or audio preprocessors, based on the type of audio samples or requirements.

  3. Observer Pattern: The Observer pattern can be used to notify users or clients about the recognition results or progress during the matching process.

  4. Decorator Pattern: The Decorator pattern can be used to add additional functionality or features, such as audio preprocessing techniques or audio quality enhancement algorithms, to the core classes in the system.

  5. Proxy Pattern: The Proxy pattern can be used to handle communication between clients and the actual audio fingerprinting service, providing a level of indirection and encapsulation for network operations.

Code: Classes Detailed Implementation Based on Patterns Mentioned Above

// AudioFingerprintGenerator interface
interface AudioFingerprintGenerator {
    AudioFingerprint generateFingerprint(AudioSample audioSample);
}

// AudioSample class
class AudioSample {
    private byte[] audioData;
    // Other attributes and methods

    public AudioSample(byte[] audioData) {
        this.audioData = audioData;
    }

    // Getters and setters
}

// AudioFingerprint class
class AudioFingerprint {
    private byte[] fingerprintData;
    // Other attributes and methods

    public AudioFingerprint(byte[] fingerprintData) {
        this.fingerprintData = fingerprintData;
    }

    // Getters and setters
}

// DatabaseManager class (Singleton)
class DatabaseManager {
    private static DatabaseManager instance;
    private Map<AudioFingerprint, MusicTrack> fingerprintTrackMap;

    private DatabaseManager() {
        this.fingerprintTrackMap = new HashMap<>();
    }

    public static synchronized DatabaseManager getInstance() {
        if (instance == null) {
            instance = new DatabaseManager();
        }
        return instance;
    }

    public void addFingerprint(AudioFingerprint fingerprint, MusicTrack track) {
        fingerprintTrackMap.put(fingerprint, track);
    }

    public MusicTrack findMatch(AudioFingerprint fingerprint) {
        return fingerprintTrackMap.get(fingerprint);
    }

    // Other database operations
}

// MusicTrack class
class MusicTrack {
    private String title;
    private String artist;
    private String album;
    // Other attributes and methods

    public MusicTrack(String title, String artist, String album) {
        this.title = title;
        this.artist = artist;
        this.album = album;
    }

    // Getters and setters
}

// RecognitionObserver interface
interface RecognitionObserver {
    void onRecognitionResult(MusicTrack track);
}

// RecognitionService class
class RecognitionService {
    private AudioFingerprintGenerator fingerprintGenerator;
    private DatabaseManager databaseManager;

    public RecognitionService(AudioFingerprintGenerator fingerprintGenerator, DatabaseManager databaseManager) {
        this.fingerprintGenerator = fingerprintGenerator;
        this.databaseManager = databaseManager;
    }

    public void recognizeAudioSample(AudioSample audioSample, RecognitionObserver observer) {
        AudioFingerprint fingerprint = fingerprintGenerator.generateFingerprint(audioSample);
        MusicTrack track = databaseManager.findMatch(fingerprint);
        observer.onRecognitionResult(track);
    }
}

// Main Class
public class MusicRecognitionSystem {
    public static void main(String[] args) {
        // Create audio sample from byte array
        byte[] audioData = new byte[]{/* Audio data bytes */};
        AudioSample audioSample = new AudioSample(audioData);

        // Create music track
        MusicTrack track = new MusicTrack("Song Title", "Artist Name", "Album Name");

        // Create audio fingerprint
        AudioFingerprint fingerprint = new AudioFingerprint(/* Fingerprint data bytes */);

        // Create database manager and add fingerprint-track mapping
        DatabaseManager databaseManager = DatabaseManager.getInstance();
        databaseManager.addFingerprint(fingerprint, track);

        // Create recognition observer
        RecognitionObserver observer = new RecognitionObserver() {
            @Override
            public void onRecognitionResult(MusicTrack track) {
                System.out.println("Recognized track: " + track.getTitle());
            }
        };

        // Create recognition service with fingerprint generator and database manager
        AudioFingerprintGenerator fingerprintGenerator = /* Create fingerprint generator */;
        RecognitionService recognitionService = new RecognitionService(fingerprintGenerator, databaseManager);

        // Recognize audio sample and notify observer
        recognitionService.recognizeAudioSample(audioSample, observer);
    }
}

In this code example, the AudioSample class represents an audio sample, the AudioFingerprint class represents the generated fingerprint for an audio sample, the DatabaseManager class handles the storage and retrieval of fingerprints and music tracks, and the RecognitionService class performs the audio recognition by generating fingerprints and matching them against the stored fingerprints in the database.

The code demonstrates the usage of classes based on the mentioned patterns, such as the Singleton pattern for creating a single instance of the database manager, the Factory pattern (not explicitly shown in the code) for creating different types of fingerprint generators or audio preprocessors based on requirements, the Observer pattern for notifying users or clients about recognition results, the Decorator pattern (not explicitly shown in the code) for adding audio preprocessing functionality to the core classes, and the Proxy pattern (not explicitly shown in the code) for handling communication between clients and the actual audio fingerprinting service.

Please note that this is a simplified example, and a complete implementation of a music recognition system using audio fingerprinting involves more complex components, such as audio preprocessing algorithms, feature extraction techniques, advanced matching algorithms, integration with external music databases or APIs, and optimization for real-time recognition in large-scale systems.

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