Audio Recording Module - Abstract Documentation

Purpose and Responsibility

Implements comprehensive think-aloud protocol recording and analysis for research applications. Provides cross-platform audio capture, real-time quality assessment, automatic transcription, and cognitive process classification from verbal protocols.

Key Data Structures and Relationships

Core Architecture

  • AudioRecorder: Main recording controller with platform-specific backends
  • AudioSession: Complete recording session with metadata and analysis
  • ThinkAloudAnalyzer: NLP-based cognitive process classifier
  • AudioFile: Individual recording file with quality metrics

Think-Aloud Taxonomy

  • ThinkAloudType: Planning, Execution, Monitoring, Evaluation, Struggle, Insight, Metacognition
  • EmotionalMarker: Frustration, Confidence, Confusion, Satisfaction with intensity scoring
  • CognitiveProcess: Problem-solving, pattern recognition, working memory load analysis

Main Data Flows and Transformations

Recording Pipeline

  1. Session Initialization: Participant setup, device configuration, consent verification
  2. Real-time Capture: Platform-specific audio recording with quality monitoring
  3. Buffer Management: Circular buffering with configurable retention and overflow handling
  4. Quality Assessment: SNR calculation, clipping detection, silence percentage analysis

Analysis Pipeline

  1. Transcription: Automatic speech recognition with confidence scoring
  2. Segmentation: Think-aloud segment identification and temporal boundary detection
  3. Classification: NLP pattern matching for cognitive process identification
  4. Emotion Analysis: Sentiment analysis and emotional marker extraction

Export Pipeline

  • Raw Audio: Platform-specific format preservation (WAV, MP3, AAC, FLAC)
  • Structured Transcripts: Time-aligned text with speaker identification
  • Analysis Metadata: Cognitive classifications, emotional markers, quality metrics

External Dependencies and Interfaces

Platform Audio Systems

  • macOS: Core Audio framework integration
  • iOS: AVAudioRecorder with iOS-specific permissions
  • Android: MediaRecorder with Android audio subsystem
  • Generic: Cross-platform fallback implementation

Real-time Processing

  • Threading: Separate recording thread for minimal latency
  • Memory Management: Lock-free circular buffers for audio samples
  • Signal Processing: RMS calculation, frequency analysis, noise detection

State Management Patterns

Session State Machine

Idle → Session Started → Recording Active → Recording Stopped → Analysis Complete → Export Ready

Concurrent Operations

  • Recording Thread: High-priority audio capture with minimal processing
  • Analysis Thread: Background processing for transcription and classification
  • Quality Monitor: Real-time metrics calculation without recording interruption

Error Recovery

  • Device Failure: Graceful degradation and error reporting
  • Buffer Overflow: Oldest data eviction with preservation of recent samples
  • Processing Errors: Fallback to raw audio preservation without analysis

Core Algorithms and Business Logic Abstractions

Audio Quality Assessment

  • Signal-to-Noise Ratio: Real-time SNR calculation for recording quality
  • Clipping Detection: Amplitude threshold monitoring for overload prevention
  • Silence Analysis: Adaptive threshold detection for speech activity
  • Overall Quality Scoring: Composite metric combining multiple quality factors

Think-Aloud Analysis

  • Pattern Matching: Keyword-based classification with confidence scoring
  • Temporal Segmentation: Pause-based boundary detection for think-aloud units
  • Cognitive Process Mapping: Evidence-based classification of mental operations
  • Emotional State Inference: Linguistic markers of affective states

Real-time Metrics

  • Volume Monitoring: Current audio levels for recording guidance
  • Buffer Status: Memory usage and recording duration tracking
  • Quality Indicators: Live feedback for recording optimization

Performance Considerations

  • Low-Latency Recording: Minimal processing delay for natural interaction
  • Memory Efficiency: Streaming processing without full audio buffering
  • CPU Usage: Optimized signal processing for real-time operation
  • Battery Life: Power-efficient recording for mobile platforms

Privacy and Compliance

  • Informed Consent: Configurable consent recording and verification
  • Data Retention: Automatic deletion policies with participant control
  • Anonymization: Speaker identification removal and transcript sanitization
  • GDPR Compliance: Data subject rights and processing transparency

Research Integration

  • Multi-modal Synchronization: Timestamp coordination with other data streams
  • Experimental Control: Recording triggers aligned with task presentation
  • Analysis Export: Integration with statistical software and research workflows
  • Validity Measures: Inter-rater reliability and classification accuracy metrics