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We conducted a preliminary user study to investigate the effectiveness of the Silver smart editing. Silver offers smart editing operations that help users resolve the inconsistencies that arise because of the different boundaries in audio and video. To help users visualize video, Silver provides multiple views with different semantic content and at different levels of abstraction, including storyboard, editable transcript, and timeline views.

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Based on interviews with professional video editors, we developed a video editor, called Silver, that uses metadata to make digital video editing. However, editing video remains difficult for several reasons: it is a time-based medium, it has dual tracks of audio and video, and current tools force users to work at the smallest level of detail. Our research suggests several ways in which video editing tools could use metadata to assist users in the reuse and composition of video.ĭigital video is becoming increasingly ubiquitous. Participants successfully edited video after only a short tutorial, both with and without smart editing assistance. Based on interviews with professional video editors, we developed a video editor, called Silver, that uses metadata to make digital video editing more accessible to novices. In this paper, we make recommendations on how future semantic speech editing systems can better support the requirements of radio production.ĭigital video is becoming increasingly ubiquitous. We found that semantic speech editing with automated speech recognition can be used to improve the radio production workflow, but that annotation, collaboration, portability and listening were not well supported by current semantic speech editing systems. They also emphasised that listening is an important part of production, to ensure high sound quality. Participants reported that they often work away from the office to avoid distractions, and print transcripts so they can work away from screens. They collaborated closely with the presenter of their programme to structure the contents and write narrative elements. We observed that the participants in our study wrote detailed notes about their recordings and used annotation to mark which parts they wanted to use. Through a contextual qualitative study of five professional radio producers at the BBC, we examined the existing radio production process and evaluated our semantic editor by using it to create programmes that were later broadcast. To investigate this, we developed a semantic audio editor based on a pilot study. Semantic speech editing systems allow users to edit audio using an automatically generated transcript, which has the potential to improve the production workflow.

Surprising Surprises: Keep exploring to discover new, hilarious surprises from Nick.Radio production involves editing speech-based audio using tools that represent sound using simple waveforms.

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