Audio Processing Parameter Learning for Individual Sound Adaptation
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Solution Overview
Problem
Current sound reproduction devices fail to adapt sound settings automatically to individual user preferences and environmental changes, requiring repetitive manual adjustments and often provide suboptimal audio quality due to generic settings that do not account for personal taste or situational needs.
Innovation Solution
An apparatus and method that determine audio processing parameters in a user-individual manner by analyzing audio signals during operation, using a processing parameter determination rule adjusted through reinforcement learning, to adapt sound settings to the user's specific preferences and environmental conditions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual sound adaptation is performed by the user, then individual preferences can be considered, but the process must be repeated for each sound reproduction and requires continuous user intervention
Solution Approach 1:
The system performs self-adaptation by automatically analyzing audio signals and adjusting processing parameters without requiring repeated manual user intervention. The learning algorithm continuously improves the sound adaptation based on recorded audio data, enabling the system to serve itself rather than requiring continuous user guidance for each sound reproduction event.
Solution Approach 2:
The system performs preliminary learning during user operation by recording and analyzing audio signals in advance. This preliminary action builds a database of audio characteristics and user preferences that is then used to automatically determine optimal processing parameters for future sound reproductions, eliminating the need for repetitive manual adjustments.
2Device complexity
If default manufacturer settings are used, then device complexity is reduced, but the settings do not correspond to individual user needs and provide insufficient sound reproduction
Solution Approach 1:
The system automatically determines individual user preferences by analyzing recorded audio signals and using machine learning algorithms to identify patterns in user behavior and environmental characteristics. This self-service approach eliminates the need for complex manual configuration while providing highly personalized sound reproduction that adapts to each user's unique needs.
Solution Approach 2:
The system dynamically changes processing parameters based on analyzed audio signals and identified situational parameters. By automatically adjusting parameters such as equalization, compression, and spatial audio characteristics according to the recorded audio data and user behavior patterns, the system achieves individualized sound reproduction without requiring complex static configuration settings.
3Manufacturing precision
If sound adaptation is performed for each sound reproduction, then audio quality is optimized for that specific event, but the process is time-consuming and cannot adapt to environmental changes
Solution Approach 1:
The system performs preliminary analysis of audio signals and identification of situational parameters during user operation, building a database that enables rapid automatic adaptation. This preliminary action occurs in advance of actual sound reproduction events, allowing the system to quickly determine optimal parameters without time-consuming real-time analysis, thus maintaining high audio quality while minimizing adaptation time.
Solution Approach 2:
The system uses feedback from recorded audio signals and user interaction patterns to continuously improve its automatic adaptation algorithm. By analyzing the relationship between recorded audio characteristics and user preferences, the system refines its parameter determination over time, achieving both high audio quality and rapid adaptation without requiring repetitive manual intervention for each sound event.
4Reliability
If hearing aid algorithms are trained in laboratories with limited sounds, then the training process is controlled and repeatable, but complex real-world noise situations cannot be simulated and result in user frustration
Solution Approach 1:
The system creates a digital copy of real-world auditory environments by recording actual audio signals from diverse situational contexts during user operation. This copying approach captures the complexity of real-world noise situations that cannot be simulated in laboratories, while maintaining the reliability of systematic data collection through automated recording and analysis processes.
Solution Approach 2:
The system performs self-training by automatically analyzing recorded audio signals from real-world environments and using machine learning algorithms to adapt the hearing aid algorithm to complex noise situations. This self-service approach eliminates the need for controlled laboratory training while maintaining systematic and repeatable learning through automated data processing and algorithm optimization.
Data Source
AI summary
The present invention relates to an apparatus and a method for determining audio processing parameters in dependence on at least one audio input signal.