Acoustic Vector Labeling for Video Search
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Solution Overview
Problem
Current social networking systems lack an efficient method for automatically assigning semantic labels to video files based on their content, which hinders effective video recommendation and search functionality.
Innovation Solution
A neural network is trained to analyze the audio stream of video files, converting it into vectors that are used to determine labels by mapping them to specific regions in an embedding space, allowing for automatic label assignment and recommendation of videos to users with similar interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of video files is performed, then labeling accuracy can be ensured, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary extraction of acoustic features from video files and pre-trains the neural network model with labeled training data before actual labeling. This preliminary preparation enables the model to automatically label new video files with high accuracy without requiring manual intervention for each file, thus reducing time consumption while maintaining labeling precision.
Solution Approach 2:
The patent introduces an acoustic feature vector as an intermediary between the raw audio signal and the labeling decision. The neural network processes these intermediate acoustic features to determine appropriate labels, enabling automated accurate labeling without direct human involvement in the actual labeling process.
2Adaptability or versatility
If no automatic labeling system is implemented, then system complexity remains low, but video recommendation and search functionality are ineffective
Solution Approach 1:
The neural network labeling system serves multiple functions: it labels videos for search functionality, generates tags for recommendation systems, and creates metadata for content organization. This multi-functional approach enables effective video recommendation and search capabilities while using a single integrated system rather than multiple separate components.
Solution Approach 2:
The system enables videos to label themselves automatically through the neural network processing of their acoustic features. Each video file undergoes automatic acoustic analysis and receives appropriate labels without requiring external manual intervention, thus improving recommendation capability while keeping operational complexity manageable through automation.
3Measurement precision
If acoustic feature analysis is performed on all video files, then labeling accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system extracts and analyzes only the acoustic feature vectors from video files that are most relevant for labeling, rather than processing all possible audio characteristics. The neural network is trained to focus on discriminative acoustic features that most strongly correlate with specific labels, performing partial analysis on the most informative aspects of the audio signal to achieve accurate labeling with reduced computational overhead.
Data Source
AI summary
In one embodiment, a method includes receiving a video file. The video file includes a corresponding audio stream. The method further includes accessing the audio stream, and generating, based on the audio stream, a representative vector. The vector has a particular number of dimensions. The method further includes accessing a label-embedding space, which has the same particular number of dimensions, and includes a number of regions that each correspond to a respective label. The method further includes determining a region of the label-embedding space that corresponds to the vector, the determined region corresponding to a particular label. The method further includes associating the particular label with the video file.