Auddia Inc.

Clip Interactive, LLC

Peter Shoebridge

Jeffrey J Thramann

Ben Good

Pablo Calderon Rodriguez

A method and system for identifying the beginning and ending of songs via a machine learning analysis. A machine learning model analyzes streaming audio (such as a radio broadcast) in overlapping, 3-second samples. Each sample is labeled into groups such as 'song,' “talk,” “commercial” and “transition.” Based on the location of the transition samples, an exact second a given song begins and ends in the audio stream is derivable. The model further identifies when two songs shift between one another.

Identifying shifts in audio content via machine learning

Disclosed herein is a music service that enables consumers listen to a broadcast radio station without commercials. The service operates by shifting the source channel of a radio from the broadcast radio to a streaming audio service for the duration of the commercial. In some embodiments, the service utilizes any of: a radio including native firmware/software, a mobile device such as a smart phone executing an application, cooperative integration of a radio and a mobile device, or master/slave relationship between a mobile device and a radio. The mobile device listens to the radio broadcast and determines when to shift between the radio broadcast and the streaming audio via any of audio fingerprint analysis, radio station behavioral analysis, radio station metadata, and/or radio station voice recognition analysis.

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Boulder, CO, United States of America

Peter Shoebridge