Voice Processing Adaptation for Scenario-Specific Quality and Resource Optimization
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Solution Overview
Problem
Current voice processing methods for network voice communication apply a uniform processing approach across different application scenarios, leading to inadequate voice quality in high-demand scenarios and resource wastage in low-demand scenarios, as they fail to adapt to specific requirements.
Innovation Solution
A method and device that detect the current application scenario of voice processing, determine the specific voice quality and network requirements, and configure corresponding voice processing parameters to tailor voice processing accordingly, optimizing resource usage while meeting quality standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a uniform voice processing method is applied across all scenarios, then the system is simple to implement, but voice quality requirements cannot be met in high-demand scenarios and resources are wasted in low-demand scenarios
Solution Approach 1:
The patent implements dynamic voice processing by detecting the current application scenario and automatically adjusting processing parameters accordingly. The system transitions from static uniform processing to dynamic adaptive processing, where the processing strength and methods change based on real-time scenario detection, thereby resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent changes processing parameters (such as noise suppression level, echo cancellation strength, encoding bitrate) based on detected application scenarios. By adjusting parameters dynamically rather than maintaining fixed uniform settings, the system achieves scenario-specific optimization without requiring completely separate processing systems for each scenario.
2Reliability
If high-quality voice processing is applied in all scenarios, then voice quality requirement is met, but system resources are wasted in scenarios with low quality requirements
Solution Approach 1:
The patent applies local quality by providing different levels of voice processing quality matched to specific application scenarios. High-quality processing (including full echo cancellation, noise suppression, and advanced encoding) is applied only when needed, while low-quality processing is used for scenarios where basic communication suffices, thereby optimizing the balance between voice quality and resource consumption.
Solution Approach 2:
The patent implements partial processing action by applying only the necessary level of voice processing for each scenario. Instead of always applying full processing, the system selectively applies processing steps based on scenario requirements, avoiding excessive processing in low-demand scenarios and reducing resource waste.
3Loss of energy
If low-quality voice processing is applied in all scenarios, then system resources are saved, but voice quality requirements cannot be met in high-demand scenarios
Solution Approach 1:
The patent implements feedback mechanisms by detecting the current application scenario and using this information to adjust processing parameters. The system continuously monitors scenario conditions and adapts processing strength accordingly, ensuring that sufficient processing is applied to meet quality requirements when needed while conserving resources when quality demands are lower.
4Productivity
If scenario detection and adaptive parameter configuration are implemented, then voice processing is optimized for specific scenarios, but system complexity increases
Solution Approach 1:
The patent segments the voice processing system into distinct functional modules: scenario detection module, parameter configuration module, and voice processing execution module. This segmentation allows each module to perform its specific function independently, making the overall complex system more manageable and maintainable while achieving scenario-specific optimization.
Data Source
AI summary
A voice processing method and device, the method comprising: detecting a current voice application scenario in a network (S1); determining the voice quality requirement and the network requirement of the current voice application scenario (S2); based on the voice quality requirement and the network requirement, configuring voice processing parameters corresponding to the voice application scenario (S3); and according to the voice processing parameters, conducting voice processing on the voice signals collected in the voice application scenario (S4).