Video Clip Extraction Using Audio Confidence Thresholds
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Solution Overview
Problem
Conventional methods for extracting video clips based on audio features in videos often result in inaccurate extraction results.
Innovation Solution
A method and apparatus that acquire a video, extract an audio stream, determine the confidence of preset audio features within continuous periods, and extract target video clips corresponding to audio clips with a confidence threshold above a preset level, using pre-trained models or feature traversal for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If video clip extraction is performed based on audio feature of audio stream, then the extraction process can be simplified, but the extraction accuracy deteriorates
Solution Approach 1:
The audio stream is divided into multiple audio frames, and each frame is independently analyzed for preset features. This segmentation allows the system to process audio data in manageable units while maintaining high accuracy in identifying target video clips through frame-by-frame confidence evaluation.
Solution Approach 2:
A pre-trained model is introduced as an intermediary component between the audio stream and the video clip extraction process. This model automatically identifies preset features in audio frames and generates confidence scores, serving as a bridge that enhances extraction accuracy without significantly increasing system complexity.
2Measurement precision
If confidence threshold filtering is applied to audio frames, then the precision of target video clip identification is improved, but the processing time increases
Solution Approach 1:
Audio frames are pre-processed and evaluated by the pre-trained model before the main extraction decision is made. The model generates confidence scores for each frame in advance, allowing the system to quickly filter frames based on the confidence threshold during the extraction phase, thereby reducing overall processing time while maintaining high precision.
Solution Approach 2:
The system dynamically adjusts the confidence threshold parameter based on different extraction scenarios and requirements. By optimizing this parameter, the system can balance between identification precision and processing speed, achieving high accuracy without excessive processing time penalties.
Data Source
AI summary
The present disclosure discloses a method and apparatus for extracting a video clip, relates to the field of artificial intelligence technology such as video processing, audio processing, and cloud computing. The method includes: acquiring a video, and extracting an audio stream in the video; determining a confidence that audio data in each preset period in the audio stream comprises a preset feature; and extracting a target video clip corresponding to a location of a target audio clip in the video; wherein the target audio clip is an audio clip within a continuous preset period, and has a confidence that the audio data includes the preset feature, which is larger than a preset confidence threshold. This method may improve the accuracy of extracting a video clip.