Audio Scene Analysis for Automated Descriptive Text Generation
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Solution Overview
Problem
Manual generation of high-quality textual content that includes relevant information while omitting non-relevant information is time-consuming and complex, especially when dealing with numerous real-world events and objects, necessitating an automated solution.
Innovation Solution
Systems and methods for analyzing audio and image data to identify objects and events, selecting adjectives, and generating descriptive textual content, which can include or exclude descriptions based on predefined groups or associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of textual content is used to describe real-world events and objects, then the quality and relevance of the text can be maintained, but the time consumption and complexity increase significantly
Solution Approach 1:
The patent introduces an automated text generation system that acts as an intermediary between audio input and textual output. This system uses speech-to-text conversion, natural language processing, and template-based generation to automatically create high-quality textual content from audio recordings, eliminating the need for manual transcription and description while maintaining text quality standards.
Solution Approach 2:
The patent replaces the manual mechanical process of text generation with an automated computational system. The system uses algorithms for speech recognition, natural language understanding, and automated content generation to substitute the human manual process, dramatically reducing time consumption while maintaining or improving text quality through consistent application of processing rules.
2Measurement precision
If manual generation of textual content is used to report numerous real-world events and objects, then accuracy can be maintained, but the complexity of the task becomes unmanageable
Solution Approach 1:
The patent segments the complex task of generating textual content about multiple events and objects into distinct automated processing stages: audio segmentation into individual events, object identification and classification, attribute extraction, and template-based description generation. This segmentation allows the system to handle numerous events and objects systematically, maintaining accuracy through structured processing while reducing overall task complexity.
Solution Approach 2:
The system enables self-service automation where the computational system automatically performs all text generation tasks without human intervention. The automated system identifies events, extracts object attributes, selects appropriate descriptions, and generates final textual content independently, making the complexity unmanageable for manual processing while maintaining reporting accuracy through consistent algorithmic application.
3Productivity
If automated text generation is implemented to handle numerous events and objects, then productivity increases, but the ability to maintain high-quality relevant content may deteriorate
Solution Approach 1:
The patent employs parameter changes in the form of adjustable processing thresholds, confidence levels, and quality filters. The system can dynamically adjust these parameters based on the volume and complexity of input data, maintaining content quality by filtering low-confidence generated text or applying more rigorous validation rules when quality standards are prioritized, while still achieving high productivity through automated processing of routine content.
Solution Approach 2:
The system incorporates feedback mechanisms where generated textual content is evaluated against quality criteria, and the system adjusts its generation parameters based on this feedback. Quality metrics are continuously monitored, and the system learns from evaluation results to improve future content generation, ensuring that high productivity does not compromise content quality but rather enhances it through iterative refinement.
Data Source
AI summary
Systems, methods and non-transitory computer readable media for analyzing audio data for text generation are provided. Audio data captured using at least one audio sensor may be received. The audio data may be analyzed to identify a plurality of objects. For each object of the plurality of objects, data associated with the object may be analyzed to select an adjective, and a description of the object that includes the adjective may be generated. Further, a textual content that includes the generated descriptions of the plurality of objects may be generated. The generated textual content may be provided.