Auditory Memory Generation Using Environmental and Emotional Audio
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Solution Overview
Problem
Existing technologies lack the ability to effectively generate auditory memories that capture the essence of an event by integrating environmental and emotional attributes of audio content, limiting the user's ability to re-experience events in a meaningful way.
Innovation Solution
A computer-implemented method using machine learning algorithms to analyze audio content, determine environmental and emotional attributes, and generate an auditory memory by combining core audio with suggested environmental and emotional audio clips, allowing users to enhance and mix audio content to create a personalized auditory experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning algorithms are used to analyze audio content and generate auditory memories with environmental and emotional attributes, then the quality and immersiveness of the auditory memory is improved, but the processing time and computational complexity increase
Solution Approach 1:
The system pre-processes audio content during the recording event to extract environmental and emotional attributes in real-time, so that when the user wants to create an auditory memory, the analysis is already complete and only synthesis remains. This preliminary extraction of attributes during the event itself reduces the processing time needed later.
Solution Approach 2:
The audio processing is divided into separate independent modules: environmental attribute extraction, emotional attribute extraction, and auditory memory synthesis. Each module processes specific aspects independently, allowing parallel computation and reducing overall processing time while maintaining high quality output.
2Measurement precision
If multiple machine learning algorithms are used to determine environmental and emotional attributes, then the accuracy and depth of attribute detection is improved, but the device complexity increases
Solution Approach 1:
The complex analysis task is segmented into two separate ML algorithms: one dedicated to environmental attributes (temperature, humidity, location) and another to emotional attributes (mood, sentiment). Each algorithm is specialized and optimized for its specific domain, improving accuracy while keeping individual algorithm complexity manageable.
Solution Approach 2:
The system uses a unified audio processing pipeline that can handle multiple types of attributes (environmental and emotional) through a common framework. This multi-functional approach reduces overall system complexity by sharing infrastructure, data structures, and processing logic across different attribute types.
Data Source
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AI summary
Techniques for generating an auditory memory for an auditory event are described. An example technique includes obtaining a first audio content associated with an event in an environment. At least one attribute of the environment is determined, based on evaluating the first audio content. At least one emotional attribute associated with the event in the environment is determined, based on evaluating the first audio content. A second audio content is determined, based at least in part on the at least one attribute of the environment. A third audio content is determined, based at least in part on the at least one emotional attribute. An auditory memory including fourth audio content associated with the event in the environment is generated, based on the first audio content, the second audio content, and the third audio content.