At AiStream, we believe the future of real-time communication lies in the seamless fusion of neural networks and real-time data streams. 'Ai' represents our core deep learning algorithms, and 'Stream' represents our ultra-low-latency WebRTC architecture. Just as a filter purifies a river, AiStream purifies audio and video streams in milliseconds, stripping away chaotic background noise and acoustic echoes to deliver ultimate, pristine human connection. We are building the next generation of intelligent streaming.
Listen to the same highly noisy input processed by different algorithms.
🔊 Suggestion: Use headphones to clearly distinguish: Noisy Input → AiStream → Clean Speech → Baselines.
Hear the far-end reference, mic signal before AEC, clean near-end target, and AiStream output after echo cancellation.
🔊 Suggested listening order: Reference Far-End -> Mic Input (Echo) -> AEC Output -> Near-End Clean Target.
Breaking the limits of computing and latency, our engine solves the most intractable audio pain points in communication.
Designed for seamless real-time communication, removing industrial noise while introducing nearly zero transmission delay.
Eliminates acoustic echoes and shields against complex feedback and enclosed space reverberation, boosting intelligibility and ASR accuracy.
Provides ultra-lightweight inference models for low-power IoT devices offline, or handles massive concurrency in the cloud.
We provide framework validation metrics across multiple scenarios against industry baselines (GTCRN, DeepFilterNet, RNNoise, etc.).
| Evaluation Metric | AiStream Next-Gen | Baselines (Average) |
|---|---|---|
| Processing Latency | Ultra-Low | Often hundreds of ms buffer |
| Speech Quality (PESQ) | Outstanding | Average |
| Preservation (STOI) | Near Zero Loss | Potential frequency loss & robotic artifacts |
| SNR Improvement | Massive Suppression of Noise & Echo | Effective only for static/low noise |
| Resource Footprint (CPU/Mem) | Ultra-Lightweight, IoT Ready | High CPU usage, power constrained |
* Real-time data and detailed partner deployment metrics will be disclosed based on specific commercial test cases.