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.

Customer Story

“AudioShake saves me hours every week — but more than that, it lets me focus on patient care.”
Spokesperson
Richard Cave
Speech & Language Therapist; Director, UCL Centre for Digital Language Inclusion
Solution Used
Vocals Separation

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.

1. Music Detection & Rights Monitoring
   Automatically detect music usage across large content libraries to support rights management, compliance, and content auditing workflows.

2. Speech Analytics
   Improve transcription, speech recognition, and voice intelligence systems with cleaner speech inputs.

3. Content Classification
   Organize and classify audio content automatically using structured audio components and metadata.

4. Broadcast Monitoring
   Analyze live streams and archived content at scale to identify music, speech, and other audio events.

Frequently Asked Questions

• Does AudioShake fit into existing media asset management or analysis systems?
  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.

• How does speaker separation improve speech analytics and diarization?
  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 — this matters for call-center analytics, compliance review, and conversation intelligence.

• Can AudioShake analyze live broadcasts and streams in real time?
  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.

• What audio events can AudioShake detect and separate for analysis?
  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.