Audio Processing Capabilities¶

Overview¶

AIDDDMAP's audio processing system provides comprehensive capabilities for analyzing, processing, and generating audio data. The system supports speech recognition, audio analysis, sound event detection, and music processing.

Core Features¶

1. Speech Processing¶

Automatic Speech Recognition (ASR)
Speaker Identification
Language Detection
Emotion Recognition

2. Audio Analysis¶

Sound Event Detection
Audio Classification
Acoustic Scene Analysis
Audio Quality Assessment

3. Music Processing¶

Music Information Retrieval
Beat Detection
Chord Recognition
Melody Extraction

4. Audio Enhancement¶

Noise Reduction
Speech Enhancement
Audio Restoration
Sound Separation

Implementation¶

1. Audio Configuration¶

interface AudioConfig {
  mode: AudioMode;
  models: ModelConfig[];
  processing: ProcessingConfig;
  output: OutputConfig;
}

interface ModelConfig {
  type: ModelType;
  path: string;
  parameters: ModelParameters;
  optimization?: OptimizationConfig;
}

enum ModelType {
  ASR = "asr",
  CLASSIFICATION = "classification",
  ENHANCEMENT = "enhancement",
  CUSTOM = "custom",
}

2. Audio Processing¶

interface AudioProcessor {
  preprocessing: PreprocessingConfig;
  features: FeatureConfig;
  analysis: AnalysisConfig;
}

interface PreprocessingConfig {
  sampleRate: number;
  channels: number;
  normalization: boolean;
  filtering: FilterConfig[];
}

interface FeatureConfig {
  type: "mfcc" | "mel" | "stft";
  parameters: FeatureParameters;
  transforms: Transform[];
}

3. Speech Recognition¶

interface ASRConfig {
  model: string;
  language: string;
  beam: number;
  vocabulary?: string[];
  acoustic?: AcousticConfig;
}

interface ASRResult {
  text: string;
  confidence: number;
  timestamps: TimeSegment[];
  metadata: ASRMetadata;
}

interface TimeSegment {
  text: string;
  start: number;
  end: number;
  confidence: number;
}

Audio Pipeline¶

1. Input Processing¶

interface InputProcessor {
  source: AudioSource;
  preprocessing: PreprocessingStep[];
  validation: ValidationRule[];
}

interface PreprocessingStep {
  operation: "resample" | "normalize" | "filter";
  parameters: any;
}

2. Feature Extraction¶

interface FeatureExtractor {
  features: Feature[];
  configuration: ExtractorConfig;
  transforms: Transform[];
}

interface Feature {
  type: FeatureType;
  parameters: FeatureParams;
  postprocessing?: PostProcessing[];
}

3. Model Inference¶

interface AudioInference {
  model: AudioModel;
  batch: BatchConfig;
  acceleration: AccelerationConfig;
}

interface AudioModel {
  architecture: string;
  weights: string;
  config: ModelConfig;
  optimization: OptimizationConfig;
}

Integration Examples¶

1. Speech Recognition System¶

// Configure speech recognition
const recognizer = new SpeechRecognizer({
  model: {
    type: "transformer",
    path: "wav2vec2-base",
    language: "en",
  },
  processing: {
    sampleRate: 16000,
    channels: 1,
    features: {
      type: "mfcc",
      numFeatures: 80,
    },
  },
  decoding: {
    beam: 5,
    lmWeight: 0.5,
    wordScore: 1.0,
  },
  output: {
    format: "json",
    include: ["text", "confidence", "timestamps"],
  },
});

// Process audio
const transcription = await recognizer.transcribe({
  audio: audioData,
  config: {
    realtime: false,
    punctuation: true,
    diarization: true,
  },
});

2. Audio Event Detection¶

// Configure event detector
const eventDetector = new AudioEventDetector({
  model: {
    type: "cnn",
    path: "yamnet",
    classes: ["speech", "music", "vehicle", "animal"],
  },
  processing: {
    windowSize: 1024,
    hopSize: 512,
    features: {
      type: "mel",
      numBands: 64,
    },
  },
  detection: {
    threshold: 0.5,
    minDuration: 0.1,
    overlap: 0.5,
  },
});

// Detect events
const events = await eventDetector.detect({
  audio: audioStream,
  config: {
    continuous: true,
    callback: (event) => {
      console.log(`Detected ${event.label} at ${event.timestamp}`);
    },
  },
});

3. Music Analysis¶

// Configure music analyzer
const musicAnalyzer = new MusicAnalyzer({
  analysis: {
    tempo: {
      enabled: true,
      range: [60, 200],
    },
    key: {
      enabled: true,
      profile: "krumhansl",
    },
    chord: {
      enabled: true,
      vocabulary: "triads",
    },
    melody: {
      enabled: true,
      polyphonic: true,
    },
  },
  processing: {
    frameSize: 2048,
    hopSize: 512,
    sampleRate: 44100,
  },
});

// Analyze music
const analysis = await musicAnalyzer.analyze({
  audio: musicFile,
  requirements: {
    tempo: true,
    key: true,
    chord: true,
    melody: true,
  },
});

Performance Optimization¶

1. Hardware Acceleration¶

interface AccelerationConfig {
  device: "CPU" | "GPU" | "DSP";
  precision: "FP32" | "FP16" | "INT8";
  batchSize: number;
  threads: number;
}

2. Model Optimization¶

interface OptimizationConfig {
  quantization: QuantizationConfig;
  pruning: PruningConfig;
  distillation: DistillationConfig;
}

Best Practices¶

1. Audio Processing¶

Proper sample rate conversion
Channel handling
Noise reduction
Quality validation

2. Model Selection¶

Choose appropriate architectures
Consider resource constraints
Balance accuracy and latency
Regular model updates

3. Deployment¶

Resource optimization
Error handling
Performance monitoring
Version control

Future Enhancements¶

Planned Features
Multi-speaker ASR
Advanced audio enhancement
Real-time processing
Custom model training
Research Areas
End-to-end speech processing
Neural audio synthesis
Cross-modal learning
Low-resource ASR