Real-Time Answering Machine Detection System
Budget: €1,500 – €3,000 EUR
Project Scope:
We have a high-accuracy (94%) Answering Machine Detection (AMD) system for outbound calls in France. It currently detects humans vs. machines within 2 seconds by downloading and processing audio. We now need to convert this into a real-time streaming solution that can make decisions as fast as possible (400ms–2s max).
Current System:
• 94% accuracy on human vs. machine detection.
• Processes downloaded audio segments (2s window).
• Works with multiple audio features and classification logic.
What Needs to Be Done:
• Transform the current batch (download) approach into low-latency real-time audio streaming processing.
• Maintain a maximum detection window of 2 seconds, but allow early decision (400ms, 500ms, 1.2s…) when confidence is 100%.
• Integrate additional features for:
• Bit verification.
• Synthetic voice detection.
• Music/sound pattern detection.
• Ensure instant call routing:
• If human → transfer immediately to a callbot.
• If machine → hang up instantly.
Skills Required:
• Strong experience in real-time audio processing (WebRTC, RTP, SIP audio streams, or equivalent).
• Proficiency in speech and signal processing (e.g., VAD, MFCC, spectral analysis).
• Machine Learning/Deep Learning for audio classification.
• Experience with latency optimization in streaming systems.
• Familiarity with telephony protocols (SIP, Asterisk, FreeSWITCH, etc.) is a strong plus.
• Python/Node.js/Go/C++ (any language capable of handling low-latency audio).
Deliverables:
• Real-time streaming AMD system (replace current download method).
• Early decision logic with configurable thresholds.
• Integration of new detection features (synthetic voice, music, bit verification).
• API or direct integration with existing call system for routing.
Additional Info:
We handle high outbound call volumes (hundreds of checks per second). The system must be highly scalable and optimized for performance.
We have a high-accuracy (94%) Answering Machine Detection (AMD) system for outbound calls in France. It currently detects humans vs. machines within 2 seconds by downloading and processing audio. We now need to convert this into a real-time streaming solution that can make decisions as fast as possible (400ms–2s max).
Current System:
• 94% accuracy on human vs. machine detection.
• Processes downloaded audio segments (2s window).
• Works with multiple audio features and classification logic.
What Needs to Be Done:
• Transform the current batch (download) approach into low-latency real-time audio streaming processing.
• Maintain a maximum detection window of 2 seconds, but allow early decision (400ms, 500ms, 1.2s…) when confidence is 100%.
• Integrate additional features for:
• Bit verification.
• Synthetic voice detection.
• Music/sound pattern detection.
• Ensure instant call routing:
• If human → transfer immediately to a callbot.
• If machine → hang up instantly.
Skills Required:
• Strong experience in real-time audio processing (WebRTC, RTP, SIP audio streams, or equivalent).
• Proficiency in speech and signal processing (e.g., VAD, MFCC, spectral analysis).
• Machine Learning/Deep Learning for audio classification.
• Experience with latency optimization in streaming systems.
• Familiarity with telephony protocols (SIP, Asterisk, FreeSWITCH, etc.) is a strong plus.
• Python/Node.js/Go/C++ (any language capable of handling low-latency audio).
Deliverables:
• Real-time streaming AMD system (replace current download method).
• Early decision logic with configurable thresholds.
• Integration of new detection features (synthetic voice, music, bit verification).
• API or direct integration with existing call system for routing.
Additional Info:
We handle high outbound call volumes (hundreds of checks per second). The system must be highly scalable and optimized for performance.