Smartphone Hearing Aid App with Real-Time Noise Suppression
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Solution Overview
Problem
Existing hearing aids do not effectively suppress non-stationary background noise and lack a real-time interactive user interface for setting processing parameters to compensate for individual frequency-dependent hearing loss, leading to degraded speech perception for users with sensorineural hearing impairment.
Innovation Solution
A personal communication device, such as a smartphone, is configured with signal processing for noise suppression using spectral subtraction and dynamic range compression, employing dynamic quantile tracking and sliding-band compression, along with a graphical user interface for real-time parameter adjustment to enhance speech quality and compensate for frequency-dependent hearing loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral subtraction and dynamic quantile tracking are used for noise suppression, then speech intelligibility is improved, but computational complexity increases
Solution Approach 1:
The audio signal is divided into frequency bands using FFT analysis, allowing noise suppression to be applied selectively to different spectral regions. This segmentation enables efficient processing by focusing computational resources on specific frequency ranges where noise is most prominent, rather than processing the entire spectrum uniformly.
Solution Approach 2:
Noise estimation is performed in advance using dynamic quantile tracking on a buffer of historical audio data before the actual speech enhancement is applied. This preliminary noise characterization allows the spectral subtraction algorithm to operate more efficiently by using pre-computed noise statistics rather than calculating them in real-time during speech processing.
2Measurement precision
If sliding-band dynamic range compression is implemented, then speech quality is improved, but processing time increases
Solution Approach 1:
The frequency spectrum is divided into multiple sliding bands that can overlap and move across the frequency range. This allows dynamic range compression to be applied independently to each band with band-specific parameters, improving speech quality by preserving formant transitions while reducing overall processing time through parallel band processing.
Solution Approach 2:
The sliding-band compression uses periodic analysis frames with overlapping windows to process the audio signal continuously. This periodic processing approach maintains real-time performance by processing fixed-length frames at regular intervals while using overlap-add synthesis to ensure continuous output without gaps.
3Adaptability or versatility
If a real-time interactive user interface is added for parameter adjustment, then user customization is improved, but device complexity increases
Solution Approach 1:
A graphical user interface layer is introduced as an intermediary between the user and the complex signal processing parameters. This interface provides visual feedback and simplified controls that map user-friendly adjustments to the underlying technical parameters, allowing users to customize hearing aid settings without directly managing complex audio processing algorithms.
Solution Approach 2:
The user interface enables real-time dynamic adjustment of processing parameters during device operation. Users can modify compression ratios, noise suppression strength, and frequency-specific gains on-the-fly, with changes taking effect immediately without requiring device reconfiguration or restart, thus providing adaptability without permanent complexity increases.
4Adaptability or versatility
If smartphone-based application software is used instead of ASIC, then flexibility is improved, but power consumption increases
Solution Approach 1:
The audio processing is organized into periodic analysis frames that are processed in batches rather than continuously in real-time. This allows the smartphone's CPU to enter low-power states between processing intervals while maintaining acceptable audio quality, reducing overall power consumption compared to continuous real-time processing.
Solution Approach 2:
The application implements processing at a slightly higher computational level than strictly necessary for basic functionality, using full-precision floating-point arithmetic and comprehensive frequency analysis. However, this excessive processing is acceptable because smartphones have sufficient computational resources, and the approach enables greater flexibility in parameter adjustment and algorithm modification without hardware constraints.
Data Source
AI summary
Hearing aids for persons with sensorineural hearing loss aim to compensate for degraded speech perception caused by frequency-dependent elevation of hearing thresholds, reduced dynamic range, abnormal loudness growth, and increased temporal and spectral masking. A digital hearing aid is implemented as a smartphone application as an alternative to ASIC-based hearing aids. The implementation provides user-configurable processing for background noise suppression and dynamic range compression. Both processing blocks are implemented for real-time processing using single FFT-based analysis-synthesis. A touch-controlled graphical user interface enables the user to set and fine-tune the processing parameters in an interactive and real-time mode.