Acoustic Event Detection for Emergency Telephony
·1 min
Streaming audio event-detection with custom PyTorch model. Nine acoustic classes, log-mel extraction, VAD, caption generation (SRT/XML/JSON), Dockerized deployment.
Technologies Used #
Deep Learning:
- PyTorch for model development
- Custom neural network architecture
- Real-time inference pipeline
Audio Processing:
- Log-mel spectrogram extraction
- Voice Activity Detection (VAD)
- Streaming audio processing
Deployment:
- Docker containerization
- Production-ready deployment
- Scalable architecture
Output Formats:
- SRT (SubRip) subtitle format
- XML structured output
- JSON for API integration
Key Features #
Real-Time Detection:
- Streaming audio processing
- Low-latency event detection
- Continuous monitoring capability
Multi-Class Classification:
- Nine distinct acoustic event classes
- Emergency-specific sound detection
- High accuracy classification
Audio Analysis:
- Log-mel feature extraction
- Voice Activity Detection
- Background noise handling
Caption Generation:
- Automated event timestamping
- Multiple output format support (SRT/XML/JSON)
- Structured event metadata
Production Deployment:
- Dockerized for easy deployment
- Scalable processing pipeline
- Monitoring and logging
Use Cases #
- Emergency telephony systems
- Automated call analysis
- Safety monitoring applications
- Event logging and documentation