Voice Intelligibility Processor With Multiband Noise Compensation
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Solution Overview
Problem
Existing voice playback devices struggle with voice intelligibility in noisy environments due to practical challenges such as physical limitations of playback and noise capture devices, signal headroom, and long-term voice characteristics, leading to degraded voice quality and inaccurate intelligibility analysis.
Innovation Solution
A voice intelligibility processor (VIP) that employs digital-to-acoustic level conversion, multiband noise and voice correction, short segment analysis, and long-term profiling, along with global and per-band gain analysis, to enhance voice intelligibility by adjusting gain parameters based on device characteristics and environmental noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice playback is performed in noisy environments using conventional techniques, then voice playback functionality is provided, but voice intelligibility is degraded due to background noise masking
Solution Approach 1:
The voice signal is divided into multiple frequency bands for separate processing. Each band is analyzed and enhanced independently based on its specific intelligibility requirements, allowing targeted noise suppression in critical frequency ranges while preserving natural sound in less critical bands.
Solution Approach 2:
Different processing strategies are applied to different frequency bands based on their importance for voice intelligibility. Critical bands receive aggressive noise suppression and enhancement, while non-critical bands receive minimal processing to maintain natural sound quality.
2Reliability
If noise capture devices and processing techniques are used to enhance voice intelligibility, then voice clarity in noise is improved, but practical implementation challenges arise including physical limitations of devices and signal headroom constraints
Solution Approach 1:
The system dynamically adjusts processing parameters including gain values, frequency band boundaries, and enhancement strength based on real-time analysis of noise characteristics, voice signal properties, and device capabilities. This allows optimization of intelligibility enhancement while respecting physical device limitations.
Solution Approach 2:
The processing system continuously adapts to changing acoustic environments and signal conditions by dynamically adjusting enhancement parameters. The system monitors signal headroom, noise levels, and device performance characteristics to modulate processing intensity in real-time.
3Reliability
If aggressive noise suppression and voice enhancement processing is applied, then voice intelligibility is improved, but signal headroom is reduced and natural sound transitions are compromised
Solution Approach 1:
The system applies enhancement processing selectively to only those frequency bands and time periods where it is most needed for intelligibility, rather than uniformly across the entire signal. This partial action approach maintains signal headroom in regions where enhancement is not critical.
Solution Approach 2:
The system continuously monitors the processed output signal levels and intelligibility improvements, using this feedback to adjust enhancement strength and prevent excessive processing that would consume signal headroom. The feedback loop ensures natural transitions by detecting when enhancement should be reduced or discontinued.
4Measurement precision
If conventional voice processing is used without considering long-term voice characteristics, then processing simplicity is maintained, but accurate intelligibility analysis and natural sound quality are compromised
Solution Approach 1:
The system performs preliminary analysis of long-term voice characteristics, noise profiles, and device response functions before applying enhancement processing. This preliminary characterization enables more accurate real-time intelligibility analysis and informs adaptive processing parameter selection.
Solution Approach 2:
The system automatically characterizes the acoustic environment, noise sources, and device properties through self-monitoring and adaptive analysis, eliminating the need for manual configuration. The processing system serves itself by learning optimal parameters from the operating conditions.
Data Source
AI summary
A method comprises: detecting noise in an environment with a microphone to produce a noise signal; receiving a voice signal to be played into the environment through a loudspeaker; performing multiband correction of the noise signal based on a microphone transfer function of the microphone, to produce a corrected noise signal; performing multiband correction of the voice signal based on a loudspeaker transfer function of the loudspeaker to produce a corrected voice signal; and computing multiband voice intelligibility results based on the corrected noise signal and the corrected voice signal.