User-Specific Noise Suppression for Voice Clarity
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Solution Overview
Problem
Existing noise suppression technologies in electronic devices often use generic parameters that fail to effectively address individual user preferences, leading to suboptimal noise reduction in voice-related features due to variations in user voices and ambient environments.
Innovation Solution
Implementing user-specific noise suppression parameters determined through voice training or user voice profiles, which tailor noise suppression based on individual preferences and voice characteristics, allowing for real-time adjustment and context-aware noise filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic noise suppression parameters are used, then device complexity is reduced and ease of manufacture is improved, but noise suppression effectiveness deteriorates for individual users
Solution Approach 1:
The system performs preliminary voice training during device setup or initialization to capture user-specific voice characteristics and noise preferences. This advance preparation stores personalized parameters that are later applied during actual voice recording or communication, eliminating the need for complex real-time analysis while maintaining high noise suppression effectiveness for each user.
Solution Approach 2:
The patent implements user-specific noise suppression by dynamically adjusting suppression parameters based on individually trained voice profiles. Different users have different noise tolerance levels and voice characteristics, so the system modifies suppression strength, frequency ranges, and algorithmic parameters according to each user's trained preferences, thereby improving effectiveness without requiring complete system redesign.
2Reliability
If user-specific noise suppression parameters are implemented, then noise suppression effectiveness is improved, but device complexity increases
Solution Approach 1:
The system performs self-training by automatically analyzing user voice samples and ambient noise during designated training periods. It autonomously generates personalized suppression parameters without requiring manual configuration or complex user intervention, reducing the operational complexity despite the personalized approach.
Solution Approach 2:
The patent creates simplified digital representations (profiles) of user voice characteristics and noise preferences during training. These copied profiles are stored and reused during actual voice recording sessions, avoiding the need to perform complex real-time voice analysis and enabling efficient noise suppression with reduced computational complexity.
3Ease of operation
If generic noise suppression parameters are used, then ease of operation is improved, but adaptability to different users and environments deteriorates
Solution Approach 1:
The system performs preliminary voice training during device setup or initialization to capture user-specific voice characteristics and noise preferences. This advance preparation stores personalized parameters that are later applied during actual voice recording or communication, eliminating the need for complex real-time analysis while maintaining high noise suppression effectiveness for each user.
Solution Approach 2:
The patent implements dynamic adaptability by allowing the system to switch between generic and user-specific parameters based on whether training data is available. The system adapts its behavior from a one-size-fits-all approach to a personalized approach automatically, improving versatility while maintaining ease of operation through transparent, automatic adaptation.
Data Source
AI summary
Systems, methods, and devices for user-specific noise suppression are provided. For example, when a voice-related feature of an electronic device is in use, the electronic device may receive an audio signal that includes a user voice. Since noise, such as ambient sounds, also may be received by the electronic device at this time, the electronic device may suppress such noise in the audio signal. In particular, the electronic device may suppress the noise in the audio signal while substantially preserving the user voice via user-specific noise suppression parameters. These user-specific noise suppression parameters may be based at least in part on a user noise suppression preference or a user voice profile, or a combination thereof.