The model handles a wide range of noise conditions including crowd noise, PA bleed, wind, music bleed, and ambient environmental sound. Unlike noise suppression tools that model and subtract a noise profile, AudioShake uses AI source separation — isolating the speech signal directly — which makes it more resilient to sudden or unpredictable noise without manual configuration.

The SDK is designed for applications that require clean speech in complex acoustic environments: live broadcast and sports production, real-time captioning and transcription pipelines, voice AI and ASR preprocessing, multilingual localization, streaming infrastructure, and conferencing tools.

Yes. AudioShake's dialogue isolation model separates clean speech from background noise, crowd noise, music, and other competing audio at latencies as low as 11ms, making it suitable for live production as well as file-based workflows. The model produces two output streams simultaneously — a clean dialogue stem and a separate background stem — giving applications independent control over both.

Yes. AudioShake's Low-Latency Speech Isolation model processes speech in real time via the SDK, supporting live AI applications including voice assistants, real-time translation, call centre AI, and live captioning systems. The SDK is available for iOS, macOS, Windows, Android, and Linux and integrates into the application's audio processing pipeline without requiring a cloud round-trip.

AudioShake's API processes mixed recordings at scale and returns isolated speech stems — clean, separated audio ready for use as training inputs for ASR and speech AI models. Teams with large audio archives can convert existing content into clean speech training datasets without controlled re-recording sessions.

Yes. AudioShake isolates clean speech from mixed audio before it reaches the ASR engine, significantly reducing word error rates by removing acoustic interference. ASR engines perform best on clean, isolated speech signals. AudioShake integrates as a pre-processing step via API or SDK, separating speech before the signal reaches the ASR model.

AudioShake supports speech AI development by producing clean, separated speech training data from mixed recordings and by integrating as a pre-processing layer in live speech AI pipelines. For speech AI teams, AudioShake addresses two distinct problems: converting large audio archives into clean training data without controlled re-recording sessions, and improving real-time inference accuracy by stripping music, effects, and background noise before audio reaches the model.