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40% decrease in editing workload by employing image recognition AI and audio signal processing for analysis and adjustment

Efficient Video Editing with AI-driven Analysis and Adjustments

Mainichi Broadcasting System (MBS) is a television broadcasting station serving the Kinki wide-area region. For the 39th Suntory 10,000 People's Ninth concert hosted by the company, Monstarlab performed analysis and adjustments using image recognition AI and audio signal processing on 14,215 user-submitted videos. By streamlining the initial video editing workflow, we contributed to a 40% reduction in MBS's editing time compared to the previous year.

Challenges:

The Suntory 10,000 People's Ninth is a music concert hosted by MBS since 1983, where 10,000 people sing Beethoven's Ninth Symphony.

Since 2020, due to measures to prevent the spread of COVID-19, the participation of general chorus groups at the venue has been suspended, and instead, approximately 10,000 submitted chorus videos are played on the venue's screen.

However, because the shooting environments for the submitted videos vary, the long editing time required for the large volume of over 10,000 video data was a challenge.


Solutions:

Initially, Monstarlab divided the videos into audio and visual elements and explored the optimal technologies to be used for each.

For audio adjustment, the start of singing varied in each video, which made it time-consuming to synchronize the timing across all videos. Therefore, we sought a solution using multiple audio signal processing techniques.

As a result of our technical investigation, we successfully identified the singing part from the audio waveform using Cross-correlation and cut out noise mixed in during recording by using Signal processing in conjunction. Furthermore, we repeatedly tested hypotheses to align the start of singing by identifying a certain sound range, in order to adjust for differences in recording environments and vocal volumes.

For video cropping, we utilized image recognition AI to align the angle of view with the subject in all videos. Because photos that happened to be in the background were sometimes recognized depending on the shooting environment, we made it possible to capture the range of movement of the person within a certain area, enabling high-precision extraction.

Client

MBS

Industry

Advertising and Telecommunication

Results:

By streamlining the mechanical pre-editing process, which can be considered the preparatory stage before the MBS editing staff began manual editing, with AI, we contributed to a 40% reduction in overall editing time.


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