AI Audio Analysis for Speech, Music, and Media Content
Platforms and broadcasters that handle large audio libraries need automated intelligence to identify music content, classify recordings, and flag issues before distribution. AudioShake processes audio at scale to detect and separate music, speech, and effects, enabling automated content analysis workflows across catalogs and live streams.
Improve Audio Analysis with AI-Powered Audio Separation
Audio analysis helps organizations understand what's happening inside audio and video content at scale. Whether identifying music usage, classifying content, monitoring broadcasts, or preparing data for AI systems, analysis accuracy depends on clean source audio. AudioShake separates music, dialogue, speakers, and effects from mixed recordings, providing structured audio inputs that improve downstream analysis workflows.
“AudioShake saves me hours every week — but more than that, it lets me focus on patient care.”
Common Audio Analysis Workflows
Organizations use audio analysis to automate content review, improve metadata generation, identify copyrighted music, and prepare recordings for machine learning systems. AudioShake enables these workflows by separating and identifying audio components before analysis occurs.
Related Solutions
Flag where music appears before content review or rights checks.
Identify which tracks are present to enrich metadata and content logs.
Separate overlapping speakers into individual tracks for cleaner classification.
Frequently Asked Questions
Yes. AudioShake runs as a processing layer via the API or SDK, feeding separated stems and time-aligned metadata into the MAM, rights, or analytics tools a team already uses. Teams without engineering resources can run the same separations through AudioShake Live in the browser.
When two or more people talk over each other, a single mixed track confuses transcription and diarization models. Multi-speaker separation splits overlapping voices into individual tracks, so each speaker can be transcribed, attributed, and analyzed cleanly — which matters for call-center analytics, compliance review, and conversation intelligence.
Yes. The real-time SDK runs separation and detection at low latency, so broadcasters and platforms can monitor live streams as they air — flagging music, isolating speakers, or routing clean audio into downstream analytics without waiting for a file to finish.
AudioShake can pull apart the distinct elements inside a recording — speech, music, individual speakers, and sound effects — and return each as its own time-aligned track. That structure lets an analysis system reason about one element at a time instead of trying to interpret everything at once from a single mixed signal.
