Audio Stem Multiplication Using Incremental Learning Feedback
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Solution Overview
Problem
Existing soundscapes are not adaptive to a user's evolving environment or state, failing to provide personalized audio transmissions for various activities, and lack dynamic access to relevant information.
Innovation Solution
Implementing incremental learning to generate and multiply audio stems based on user feedback, using a network architecture that includes sensor data and sound libraries to create personalized sound environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional soundscapes are used, then the system is simple and easy to implement, but the adaptability to user's evolving environment or state is poor
Solution Approach 1:
The system dynamically adapts soundscapes to user's evolving environment and state through continuous monitoring and real-time modification. The soundscape generation system transitions from static pre-defined soundscapes to dynamic adaptive soundscapes that respond to user feedback and environmental changes, resolving the contradiction between adaptability and complexity by implementing controlled dynamic adjustments based on user needs.
Solution Approach 2:
The system incorporates user feedback loops where user responses to generated soundscapes are collected and used to refine future soundscape generation. This feedback mechanism enables the system to learn and adapt to individual user preferences over time, improving adaptability while managing complexity through iterative optimization rather than requiring complex pre-programming for all scenarios.
2Adaptability or versatility
If personalized soundscapes are generated using incremental learning, then the adaptability and personalization improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing audio stems and preparing multiple variations in advance using incremental learning. Configuration information and processing parameters are pre-computed and stored, allowing rapid generation of personalized soundscapes when needed. This approach reduces real-time processing time while maintaining high personalization capability through pre-prepared adaptive content.
Solution Approach 2:
The system applies partial processing by generating a limited set of configuration information and processing parameters focused on the most relevant audio stems and user preferences. Rather than processing all possible audio data comprehensively, the system selectively processes only the necessary portions required for effective personalization, reducing computational overhead and processing time while maintaining adequate personalization quality.
3Productivity
If multiple audio processing operations are applied to generate varied stems, then the diversity and quality of soundscapes improve, but the device complexity and processing requirements increase
Solution Approach 1:
The audio processing system is segmented into modular components, each responsible for specific processing operations on different audio stems. Configuration information organizes processing operations into discrete, manageable segments that can be independently applied and optimized. This modular segmentation allows high-quality multi-operation processing while managing system complexity through organized, reusable processing blocks.
Solution Approach 2:
The system implements universal processing operations that can be applied across multiple audio stems and different soundscape generation scenarios. Configuration information enables a single set of processing operations to serve multiple functions and generate diverse soundscapes from different input stems, reducing overall system complexity by avoiding the need for separate specialized processing chains for each case.
Data Source
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AI summary
A process can include obtaining input audio stems comprising a selected subset of a plurality of audio stems, and obtaining configuration information corresponding to audio processing operations for the input audio stems. An audio stem multiplier engine can generate a plurality of multiplied audio stems based on the input audio stems and the configuration information. Each multiplied audio stem comprises a variation of a particular input audio stem and is generated based on applying one or more audio processing operations parameterized by the configuration information. Information indicative of user feedback ratings for each respective multiplied audio stem of the plurality of multiplied audio stems can be received. The audio stem multiplier engine can generate a second plurality of multiplied audio stems based on at least the user feedback ratings and one or more of the input audio stems or the plurality of multiplied audio stems.