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Auddia Inc (NASDAQ: AUUD) Announces Major Technology Advancements in Artificial Intelligence with New Audio Content Processing Methodology

Auddia Inc (NASDAQ: AUUD) today proclaimed a significant progression in its branded skill at its Artificial Intelligence appliance center. By leveraging accurate acoustic and metadata from wireless postings, Auddia will decrease the prices of handling acoustic content for AI preparation and authentication to near zero and, alongside comprehend massively more oversized developments incorrectness. The new AI dispensation procedure gives the business near real-time data dispensation competencies, which is anticipated to advance the complete presentation of the Auddia dais and decrease the onboarding period for stations by a cause of five as the business measures to thousands of stations.

Auddia imagines the new AI procedure for its examinations with Lakes Media and Sonoma Media that were proclaimed beforehand and will begin soon after the July 4 break. The company endures accounting for customer interest and payment valuing from Lakes Media and Sonoma Media spectators expecting its full nationwide presentation in the subsequent half of 2021. Peter Shoebridge, Auddia’s Chief Technology Officer, clarified, “Our newest progression in AI grosses benefit of what we continuously assumed to be one of the most valued rudiments of the audio content system, which is the profusion and uncluttered obtainability of acoustic records. Precisely cataloging that acoustic records with exact metadata is the final purpose, and our new practice allows us to meet that purpose. Fresh test consequences that permit us to associate our new method to preceding approaches discloses orders of greatness enhancement in parts that are perilous to the industry, counting correctness, haste and appropriateness of AI drill, and the costs of process.” The latest skill progression permits Auddia to train its AI contraption with pointedly more records, in less while, and at an importantly abridged charge. The new method produces far better and earlier content identification correctness.

For instance, to train the AI archetypal on a collection of six radio stations, the preceding method demanded the tiniest of 50 hours of acoustic to attain acceptable consequences. This human-intensive procedure would take five days with firm prices over $2,100. With the newest progression, the same collection of stations can be accomplished on 1008 hours of acoustic records – a 20X upsurge in records capacity – with zero firm prices, finished in a solitary day, and produce far better correctness. When developing AI, training with more data is always preferred, but to process 1008 hours of radio audio using the previous approach would have resulted in hard costs of $40,000.

Working with significantly more audio data that comes with precise metadata, results in highly accurate AI algorithms. At the time of this announcement, the improvement in content identification accuracy is higher than 170%, potentially improving further. These algorithms are expected to produce greater accuracy in all applications, including when used against radio stations with no AI training whatsoever.

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