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.

AudioShake integrates via API and SDK, enabling automated separation within existing localization tools, captioning platforms, and post-production pipelines. Partners including OOONA, Papercup, and cielo24 have built AudioShake into their production infrastructure. The API triggers separation during ingest and returns separated stems before content reaches the dubbing, transcription, or captioning stage.

Yes. AudioShake's M&E Separation model separates audio into three independent components — dialogue, music, and effects — in a single pass, with each exported as a separate file. Teams can select which stems they need for a given job. One processing step serves multiple downstream needs — the same run that produces the M&E track for dubbing also produces the clean dialogue stem for captioning and transcription.

An M&E (Music and Effects) track contains all programme audio except the original dialogue. It is the foundation of any dubbed version, allowing new recorded speech to be laid over the original soundtrack. AudioShake's M&E Separation model produces a clean M&E stem and an isolated dialogue stem directly from a finished recording, removing the dependency on original session access that creates bottlenecks in localization workflows.

Isolating clean speech before applying ASR significantly reduces word error rates. Music, effects, and ambient noise in mixed audio all degrade transcription accuracy when the full mix is passed directly to a speech engine. For localization teams this reduces manual correction time, improves consistency across languages, and makes automated captioning viable on content that would previously have required human transcription due to noise levels.

AudioShake's M&E Separation model produces a clean dialogue stem and an intact M&E track from a fully mixed recording in a single pass, giving localization teams both deliverables required for dubbing without original session files.