Video Synopsis Audio Analysis Neural Network
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Solution Overview
Problem
Existing methods for creating 'catch-up' content from streaming video are largely manual and inefficient, making it difficult to generate a time-compressed synopsis in real-time during live streaming events.
Innovation Solution
An automated system that extracts and analyzes the audio signal from streaming video content using a domain-specific neural network image classification algorithm to select and stitch together relevant segments for a video synopsis, minimizing analysis to a single audio signal and correlating it with corresponding video segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to create catch-up content, then the content can be assembled with human judgment, but it is difficult to create catch-up content in real time and in a flexible manner
Solution Approach 1:
The system performs self-service by automatically analyzing audio tracks, generating interest probability scores, and selecting video segments without requiring manual human intervention. The automated pipeline processes audio signals, correlates them with video content, and assembles synopses in real-time, eliminating the need for manual content assembly while maintaining flexible and adaptive content selection.
2Measurement precision
If known techniques for creating spectrographs are applied, then audio segments can be classified, but they are too slow to be applied in real time
Solution Approach 1:
The system extracts only the essential audio features needed for classification by focusing on specific frequency bands and temporal patterns rather than processing the entire audio signal. By selecting and analyzing only the most relevant audio characteristics, the system achieves accurate classification while significantly reducing processing time to enable real-time operation.
Solution Approach 2:
The system changes the parameters of audio analysis by transforming the audio signal into different representational spaces (e.g., spectrograms, mel-frequency cepstral coefficients) and adjusting analysis parameters such as window size, step size, and frequency resolution. These parameter optimizations allow the system to maintain classification accuracy while achieving real-time processing speeds.
3Reliability
If comprehensive audio analysis is performed on the entire audio track, then all relevant segments can be identified, but the analysis time increases significantly
Solution Approach 1:
The system segments the audio track into smaller temporal units (frames or chunks) and processes them individually through the neural network classifier. By dividing the large audio signal into manageable segments and analyzing them sequentially or in parallel, the system maintains comprehensive coverage of all relevant content while significantly reducing total analysis time compared to processing the entire track as a single unit.
Data Source
AI summary
Systems, methods, and storage media for selecting video portions for a video synopsis of streaming video content are disclosed. Exemplary implementations may: extract at least a portion of an audio track from a live stream of video content over time to create an audio file; convert the audio file from a time domain to a frequency domain; generate a visual representation of the spectrum of frequencies of the audio signal as it varies with time; apply a classification algorithm to the visual representation to generate interest probability scores for portions of the audio signal; select portions of the audio signal that meet or exceed a threshold probability score; correlate the selected portions of the audio signal to corresponding segments of the video content that has been streamed; and select the corresponding segments of the video content for inclusion in the synopsis.