A first of its kind AI-solution that uses unsupervised learning to correlate audio data and get smarter over time. Our unique design approach does not use modeling or extensive training data but rather uses inferencing AI to adapt to the dynamically changing auditory scene.

A voice-biometric driven tracking methodology for effective voice from noise and voice from other voices isolation. A byproduct of this approach is the ability to extract perceptual data (identity, emotion, direction, range, gender) from an individual voice within a noisy signal.

IP that repairs and enhances the signal of interest for a better listening experience, improved performance for automatic speech recognition (ASR), effective metadata extraction, and accurate speaker identification. In addition, our unique method of signal enhancement does not introduce unnatural artifacts.