Context-Based Sound Association for Digital Text Immersion
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Solution Overview
Problem
Conventional systems for associating sounds with digital text fail to provide immersive experiences, as they lack context-based sounds anchored to specific words or phrases, and require costly and technically expertise-intensive manual recording processes.
Innovation Solution
A sound association system that uses a multimodal classification module to automatically identify and associate context-based sounds with text, employing a text classification module, a sound classification module, and an additional classification module to generate embeddings and predict sound tags, allowing for the automatic selection and playback of relevant sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional soundtrack systems are used to provide background music for digital books, then users can have musical accompaniment during playback, but the system fails to provide immersive experiences and context-based sounds anchored to specific words or phrases
Solution Approach 1:
The system automatically associates context-based sounds with text by analyzing the text content itself and selecting appropriate sounds from a library, eliminating the need for manual recording and post-production work. The automated sound association system processes text and generates sound selections without human intervention, making the system self-sufficient for sound production tasks.
Solution Approach 2:
The patent replaces the mechanical process of manual sound recording and editing with an automated computational system that uses text analysis and machine learning algorithms to select and associate sounds with text passages, substituting human labor with automated processing.
2Reliability
If manual Foley sound recording is used to create context-based sounds for digital content, then high-quality immersive sounds can be achieved, but the cost and technical expertise requirements create significant barriers to entry
Solution Approach 1:
The system uses a pre-existing library of recorded sounds and selectively associates them with text passages based on automated analysis, copying appropriate sounds from the library rather than creating new recordings. This approach maintains sound quality while eliminating the need for specialized recording equipment and expertise.
Solution Approach 2:
The automated sound association system is designed to handle multiple text inputs and select from a diverse sound library, making it a universal solution that can process various types of digital content without requiring specialized manual intervention for each project.
3Ease of operation
If no sounds are associated with digital content due to cost constraints, then production costs remain low, but users experience frustration and lack of engagement
Solution Approach 1:
The system automatically generates sound associations for digital content without requiring manual intervention, enabling creators to add immersive audio elements to their content efficiently. The automated process handles sound selection and association, making content creation more productive while enhancing user engagement.
Data Source
AI summary
A sound association system identifies one or more aurally active words in digital text. Aurally active words refer to words that denote particular sounds. Context-based sounds corresponding to the one or more aurally active words are also identified. Each context-based sound is anchored to or associated with the corresponding one or more aurally active words and is played back when the digital text is played back or read, providing context-based background sounds associated with the one or more aurally active words. For example, a context-based sound can be played back at a higher volume when the one or more aurally active words are played back or read, and at a lower volume when other words of the digital text are played back or read.