Audio Embedding Search for Video Event Classification
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Solution Overview
Problem
Analyzing vast amounts of video content to retrieve relevant content based on sound features is cumbersome and inefficient.
Innovation Solution
A video management system (VMS) utilizing pretrained text-audio encoders to encode audio signals and text prompts, enabling the ranking and retrieval of relevant video content based on audio embeddings, allowing users to search video libraries by describing sound features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of video content is used to retrieve relevant content, then comprehensive search capability is achieved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system extracts audio features from video content in advance and stores them as audio embeddings in a vector database before any search operation occurs. This preliminary indexing of audio characteristics enables rapid retrieval during search operations without requiring real-time analysis of the entire video library, thus resolving the contradiction between search accuracy and time consumption.
Solution Approach 2:
The patent replaces manual mechanical analysis of video content with an automated audio-based search system using text encoders and vector database technology. Users can search by describing sound features in natural language, which are converted to embeddings and compared against stored audio embeddings, eliminating the need for manual video review while maintaining comprehensive search capability.
2Loss of information
If comprehensive video content analysis is performed, then all relevant information is captured, but system complexity and computational resources increase
Solution Approach 1:
The system extracts and isolates audio features from video content as a separate searchable dimension, storing them as independent audio embeddings in a vector database. This extraction approach allows the system to maintain information completeness by preserving audio characteristics while simplifying the search process to focus on audio embeddings rather than analyzing entire video files, thus reducing system complexity.
Solution Approach 2:
The patent segments the video content analysis into distinct components: audio feature extraction, embedding generation, and vector storage. This segmentation allows each component to be optimized independently and enables efficient retrieval by focusing only on the relevant audio dimension, reducing overall system complexity while maintaining information completeness.
3Productivity
If traditional video search methods are used, then system simplicity is maintained, but search efficiency and user experience deteriorate
Solution Approach 1:
The system implements a multi-functional audio search capability that handles various search scenarios (sound effect identification, speech search, environmental noise detection) through a unified audio embedding framework. This universal approach improves search efficiency across different use cases while managing complexity through a single coherent architecture rather than multiple specialized systems.
Solution Approach 2:
The patent introduces text encoders and embedding vectors as intermediary representations between user queries and video content. These intermediaries transform natural language descriptions and audio features into a common mathematical space, enabling efficient comparison and retrieval while abstracting away the complexity of direct video analysis from the user interface.
Data Source
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AI summary
A method includes defining, by a text encoder, a set of text embeddings for a text prompt indicative of a search query for video content having audio data indicative of a sound feature that is defined as a search parameter of the search query, and ranking a plurality of audio embeddings indicative of a plurality of audio signals and provided in a vector database using the set of text embeddings of the search query. The method further includes detecting a relevant audio record associated with an identified audio embedding from among the ranked audio embeddings, and outputting a relevant video content associated with the relevant audio record to have a computing device play the video content, the relevant video content being obtained from among a plurality of video content.