Text-Driven Soundscape Generation for Adaptive Audio Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional soundscapes are not adaptive to a user's evolving environment or state, and they lack personalization, failing to provide dynamic and context-specific audio experiences for activities like relaxation, focus, or storytelling.
Innovation Solution
A system that generates a continuous soundscape using automatic composition based on text data and sensor inputs, utilizing a machine learning network to analyze text frames and determine sound sections that adapt to the user's environment and state, synchronizing playback with reading position and providing real-time personalized sound environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional soundscapes are used, then the system is simple and easy to implement, but the soundscapes are not adaptive to user's evolving environment or state and lack personalization
Solution Approach 1:
The system dynamically adjusts soundscapes in real-time based on changing user states and environmental conditions. The machine learning network continuously processes sensor data and text inputs to generate adaptive sound compositions that evolve with the user's context, transforming static soundscapes into dynamic, responsive audio experiences.
Solution Approach 2:
The system implements feedback loops where sensor data from the user's environment and state is continuously collected, analyzed by the machine learning network, and used to adjust the soundscape parameters. This closed-loop feedback mechanism enables the system to adapt to user needs automatically, with the machine learning model learning from ongoing interactions to improve personalization over time.
2Adaptability or versatility
If homogeneous soundscapes of limited length are used, then the system is simple, but they are not adaptive and lack contextual relevance
Solution Approach 1:
The system performs preliminary analysis of text inputs and sensor data to pre-determine appropriate soundscape characteristics before actual playback. The machine learning network processes and categorizes contextual information in advance, preparing sound composition parameters that can be quickly deployed when needed, reducing real-time processing delays.
Solution Approach 2:
The system generates continuous, evolving soundscapes that maintain contextual relevance throughout extended periods. Rather than using discrete, limited-length audio clips, the machine learning network continuously composes and adjusts sound parameters to match ongoing user context, ensuring uninterrupted adaptive audio experiences for activities like reading or relaxation.
3Loss of information
If audio compositions are added to convey contextual information, then user engagement and comprehension are improved, but the system complexity increases
Solution Approach 1:
The machine learning network serves multiple functions simultaneously: it analyzes text inputs for contextual meaning, processes sensor data for user state detection, determines appropriate soundscape parameters, and synchronizes audio playback with reading position. This multi-functional approach consolidates what could be separate complex systems into a single integrated intelligence core.
Solution Approach 2:
The machine learning network acts as an intermediary layer between the raw inputs (text and sensor data) and the audio output system. It translates contextual information from text and environmental data into appropriate soundscape parameters, mediating between diverse input modalities and the audio generation process while managing system complexity through this intermediate processing layer.
Data Source
AI summary
Disclosed are systems and techniques for creating a personalized sound environment for a user. A process can include obtaining text data comprising a plurality of words. A plurality of text frames are generated based on the text data, each respective text frame including a subset of the plurality of words. A machine learning network can be used to analyze each respective text frame to generate one or more features corresponding to the respective text frame and the subset of the plurality of words. Two or more sound sections can be determined for presentation to a user, each sound section corresponding to a particular text frame of the plurality of text frames and generated based at least in part on the one or more features of the particular text frame. A personalized sound environment is generated to include at least the two or more sound sections and is presented to the user on a user computing device.