Automated Caption Timestamp Prediction via Audio Threshold Analysis
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Solution Overview
Problem
Adding additional subtitles to video content can be time-consuming, as individuals need to manually identify suitable time ranges for inserting non-verbal and contextual captions, which requires reviewing the content and is inefficient.
Innovation Solution
An automated solution that extracts audio data from video files, compares it against a sound threshold to identify auditory timeframes, and parses subtitle data to find subtitle-free timeframes, thereby determining candidate time ranges for adding additional captions, which can be merged or omitted to generate final timestamps for caption insertion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of time ranges is used to add subtitles, then accuracy of caption placement can be controlled, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables self-service by having the video content itself provide the information needed for caption placement. The audio track automatically identifies sound events and their time ranges, eliminating the need for manual analysis of the video content. The system uses the video's own audio characteristics to determine where captions should be placed, making the process autonomous and time-efficient.
Solution Approach 2:
The patent replaces the mechanical manual process of reviewing video content and identifying time ranges with an automated audio-processing system. Instead of human operators visually and auditorily analyzing the video, the system uses computational algorithms to process audio data, detect sound events, and generate timestamp information automatically.
2Reliability
If manual review of video content is performed to identify suitable time ranges, then quality of caption integration can be ensured, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically analyzing the video's audio track to identify sound events and determine appropriate caption placement time ranges. The video content itself provides the metadata needed for processing, eliminating the need for human review while maintaining consistent and reliable caption integration through automated detection algorithms.
Solution Approach 2:
The system changes the parameters for caption placement by using audio-based detection (sound threshold, sound event duration, inter-event time) instead of manual time range identification. This parameter transformation from manual temporal selection to automated acoustic feature analysis enables both high reliability and improved productivity.
Data Source
AI summary
An automated solution to determine suitable time ranges or timestamps for captions is described. In one example, a content file includes subtitle data with captions for display over respective timeframes of video. Audio data is extracted from the video, and the audio data is compared against a sound threshold to identify auditory timeframes in which sound is above the threshold. The subtitle data is also parsed to identify subtitle-free timeframes in the video. A series of candidate time ranges is then identified based on overlapping ranges of the auditory timeframes and the subtitle-free timeframes. In some cases, one or more of the candidate time ranges can be merged together or omitted, and a final series of time ranges or timestamps for captions is obtained. The time ranges or timestamps can be used to add additional non-verbal and contextual captions and indicators, for example, or for other purposes.