– Case Study –
Audio Annotation for Intelligent Podcast Ad Insertion
After evaluation of thousands of podcasts to create audio training data, this AI platform inserts advertisements at ideal points in the podcast
A global podcast company, which provides podcast hosting, analytics, and ad marketing, needed a way to automatically insert ads at the appropriate locations in podcasts, such as during a segment change or transition. This “dynamic ad insertion” for podcasts involves stitching ads into raw audio files the moment that a request is received to download these audio files. The company required a training dataset of ad placements to be able to train its platform to insert the ads automatically at the appropriate locations.
thousands of hours of audio training datasets
In order to create the needed datasets, Innodata began by evaluating the ad placement of thousands of podcasts. While listening to the audio files, the team identified and labeled the quality of ad placements to help train the model where to auto-insert advertisements. Attached to the evaluation of the labeled audio content was the subjective reasoning for the assigned labels. To ensure the quality of the processed dataset, Innodata utilized a double pass blind process, which involved separate evaluation of each podcast by two annotators and arbitration by a senior annotator. The team leveraged its proprietary platform to annotate, review, provide kappa scores on a weekly basis, and update everything in real-time.
The Innodata team delivered thousands of hours of audio training datasets. These datasets enabled the AI platform to accurately insert advertisements at appropriate times and locations in a podcast without disrupting the flow. This helped the company decrease the number of listeners dropping out of the podcast midstream.
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