Veritonic Announces API for Audio Scoring

Veritonic, the industry's leading audio research and analytics platform, announced today the availability of a first-of-its-kind API for audio scoring. With Veritonic's API, anyone analyzing audio - including publishers, agencies, and technology vendors - can seamlessly view audience engagement scores in whatever system or tools they use.

Veritonic
Veritonic

Powered by Veritonic's Machine Listening and Learning (M-LALTM) technology, the API provides users with instant access to audio measurement scores for several efficacy attributes including recall, engagement, and intent for every asset in their library. Publishers can then use these scores to make informed decisions around when and where to use their assets, and how they can be further optimized to increase ROI. Every type of audio asset can be scored through this API, including audio logos, audio ads, podcast ads, functional sounds, music, podcast clips, voiceovers, and more.

"We remain committed to providing the audio industry with seamless access to the data and insights that are elevating campaign performance and increasing ROI," said Scott Simonelli, CEO of Veritonic. "Our API enables critical audio efficacy metrics to sit alongside an asset for the first time, unlocking unprecedented scale and making analysis and decision-making faster and more streamlined than ever before."

"We look forward to leveraging Veritonic's API to further integrate audio measurement into our intelligent Sonic OS platform, the one-stop-shop for data-aided evaluation and implementation of sonic identities," said Michele Arnese, Founder and CEO / CCO of amp sound branding. "This API will enable us to simplify the process of evaluating and optimizing our assets at all stages of the sonic branding development process, creating even more impactful and measurable audible experiences for our clients."

Veritonic's Audio Score is powered by its Machine Listening and Learning technology (M-LALTM), offering publishers a fast, credible way to determine the performance of audio based on years of data, research, and comparative assets.

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