Feature-Based Gain Control for Hearing Prosthesis Clipping
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Solution Overview
Problem
Automatic Gain Control (AGC) systems in hearing prostheses face challenges in managing dynamic range, leading to excessive loudness clipping and distortion due to fixed gain settings, which affect the accuracy of sound processing and user experience.
Innovation Solution
A computer-implemented method and device that determine features of a subject signal, revise control signals based on these features, and adaptively apply loudness growth functions (LGFs) to compress the signal, allowing for dynamic adjustment of gain settings in multiple frequency bands to prevent clipping and improve sound processing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed gain settings are used in AGC systems, then device complexity is reduced, but loudness clipping and distortion increase
Solution Approach 1:
The patent implements dynamic gain adjustment by continuously monitoring signal features (zero-crossing rate, spectral flux, chroma) and adapting control parameters in real-time. This transforms the static fixed gain system into a dynamic system that responds to changing audio conditions, preventing clipping and distortion while maintaining simplicity through algorithmic control rather than hardware complexity
Solution Approach 2:
The system changes control parameters (gain values, attack/release times) based on extracted audio features. By monitoring features like zero-crossing rate and spectral flux, the system adjusts gain parameters dynamically to match the input signal characteristics, resolving the contradiction between simple fixed control and the need to prevent distortion
2Measurement precision
If dynamic gain adjustment is implemented, then sound processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the audio signal analysis into distinct feature extraction components: zero-crossing rate detection, spectral flux calculation, and chroma extraction. Each component processes a specific aspect of the signal independently, then combines results to control gain. This segmentation improves accuracy while managing complexity by breaking down the processing into modular, manageable tasks
Solution Approach 2:
The extracted audio features serve multiple functions: they control gain adjustment, detect signal onset, and characterize signal type. This multi-functionality reduces overall system complexity by using a single feature extraction module to drive multiple control decisions, improving sound processing accuracy without proportionally increasing device complexity
3Adaptability or versatility
If feature-based control signals are used, then adaptability to different sound conditions is improved, but processing time increases
Solution Approach 1:
The system performs preliminary feature extraction (zero-crossing rate, spectral flux, chroma) on incoming audio signals to pre-characterize the signal before full processing. These pre-extracted features are then used to quickly determine appropriate gain control actions, enabling rapid adaptation to different sound conditions without requiring extensive real-time analysis during the critical gain adjustment phase
Data Source
AI summary
A computer-implemented method comprising: determining one or more features of a subject signal; revising one or more control signals on the basis of the one or more features; modifying a level of the subject signals based on the control signals. At least one of the features is determined by: comparing a given one of the subject signals against a boundary signal to produce a corresponding given boundary comparison signal; and summarizing the behavior of the given boundary comparison signal over a time interval.