TAREA 07: Anomaly Detection - Audio Quality Monitoring¶

Status: 🔴 PLANNING

🎯 Purpose¶

Real-time audio quality monitoring, glitch detection, and artifact identification using unsupervised learning.

🏗️ Key Components¶

Autoencoder-Based Detection: Reconstruction error anomaly detection
Statistical Outlier Detection: Z-score, isolation forest
Real-Time Monitoring: Stream-based anomaly detection
Artifact Detection: Clicks, pops, distortion, dropouts

📋 Architecture¶

class AnomalyDetector {
public:
    // Detect anomalies in audio buffer
    AnomalyReport detectAnomalies(const std::vector<float>& audio);

    // Real-time monitoring
    void monitorRealTime(const float* audio, int num_samples,
                        std::function<void(Anomaly)> callback);

    // Train on normal audio
    void trainNormalProfile(const std::vector<std::vector<float>>& normal_audio);

    // Set sensitivity threshold
    void setSensitivity(float threshold);  // 0.0 - 1.0
};

struct Anomaly {
    enum Type { Click, Pop, Distortion, Dropout, Unknown };

    Type type;
    int sample_offset;
    float confidence;
    float severity;  // 0.0 - 1.0
};

🎯 Use Cases¶

Live performance monitoring
Recording quality check
Automatic error detection in mastering
Hardware failure prediction
Broadcast signal monitoring

📊 Techniques¶

Autoencoder: Learn normal audio distribution
One-Class SVM: Outlier detection
Isolation Forest: Ensemble-based anomaly detection
LSTM Autoencoders: Temporal anomaly detection

🔧 Example¶

AnomalyDetector detector;
detector.initialize("models/autoencoder_anomaly.onnx");
detector.trainNormalProfile(normal_recordings);
detector.setSensitivity(0.8f);

// Real-time monitoring
detector.monitorRealTime(audio_buffer, buffer_size,
    [](Anomaly anomaly) {
        std::cout << "Anomaly detected at sample " << anomaly.sample_offset
                  << " Type: " << anomaly.type
                  << " Severity: " << anomaly.severity << "\n";
    }
);

Priority: 🟡 Medium - Important for professional workflows