Personalized ASMR Audio Generation from Imaging Data
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Solution Overview
Problem
The abundance of ASMR content available requires manual navigation, which can be overwhelming and results in a loss of the relaxing effect due to differing user responses, necessitating a solution to personalize audio content based on user environment and mood state.
Innovation Solution
An apparatus and method that captures imaging data of a user's environment using cameras or LiDAR, processes it with a machine-learning model, and generates personalized audio content, including ASMR-inducing sounds, based on user information and mood state, which can be shared with others for a collaborative experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual navigation of ASMR content is provided, then users can access available content, but the process becomes overwhelming and loses the relaxing effect
Solution Approach 1:
The system automatically captures imaging data of the user's environment and uses machine learning models to generate personalized ASMR audio content without requiring manual selection. The apparatus self-adjusts based on environmental context and user mood state, eliminating the need for users to navigate through content libraries manually.
Solution Approach 2:
The system dynamically changes audio content parameters based on real-time imaging data and mood state information. The machine learning model adjusts audio characteristics such as sound type, intensity, and composition based on environmental context, transforming static content selection into dynamic adaptive generation.
2Adaptability or versatility
If standardized audio content is provided, then content delivery is simple, but it fails to account for differing user responses and environmental contexts
Solution Approach 1:
The system performs preliminary capture of imaging data and mood state information before generating audio content. The machine learning model is pre-trained with diverse ASMR content characteristics, enabling it to quickly adapt and generate personalized content based on the captured context without requiring complex real-time processing during content delivery.
Solution Approach 2:
The machine learning model acts as an intermediary between the captured environmental data/mood state and the generated audio content. This intermediary processes the imaging data and user information to transform them into personalized ASMR content, simplifying the overall system architecture while enabling high adaptability.
3Reliability
If personalized audio content is generated based on imaging data and mood state, then user engagement is enhanced, but processing complexity and computational requirements increase
Solution Approach 1:
The system segments the personalization process into distinct functional modules: imaging data capture, mood state detection, machine learning processing, and audio content generation. This segmentation allows each component to be optimized independently and distributed across different hardware platforms, reducing the complexity burden on any single device.
Data Source
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AI summary
An apparatus, method and computer program is described comprising: capturing imaging data relating to a user environment; providing captured imaging data to a processor for generating content based, at least in part, on the captured imaging data, wherein the generated content comprises audio content; receiving said generated content; and providing an audio output to the user based on said generated content.