Multi-Microphone Sound Identification via Power Level Filtering
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Solution Overview
Problem
Existing sound identification systems face challenges in distinguishing sound from a source of interest from background noise, particularly in low power or low cost applications, due to complexity and resource-intensive computations, and reliance on statistical models or heuristics developed through machine learning or template matching.
Innovation Solution
A sound processing system utilizing multiple microphones, where one microphone is closer to the source of interest, processes audio feeds by time synchronizing and filtering frequencies between them to identify sound originating from the point of interest, thereby reducing processing and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical models or heuristics developed through machine learning or template matching are used to distinguish sound from source of interest, then sound identification accuracy is improved, but system complexity and computational resource requirements increase
Solution Approach 1:
The patent extracts and removes the complex statistical models and machine learning components from the voice activity detection system. Instead of using sophisticated algorithms, the invention employs simple signal processing techniques such as power level difference ratio calculations and basic filtering operations that eliminate the need for resource-intensive computational models while maintaining effective noise suppression.
Solution Approach 2:
The patent replaces expensive, complex computational models with simple, computationally inexpensive operations. The system uses basic arithmetic operations (power level differences, ratios) and straightforward filtering techniques that require minimal processing resources, making the system suitable for low-power and low-cost applications without relying on sophisticated statistical models.
2Measurement precision
If statistical models or heuristics developed through machine learning or template matching are used to distinguish sound from source of interest, then sound identification accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent extracts and removes the complex statistical models and machine learning components from the voice activity detection system. Instead of using sophisticated algorithms, the invention employs simple signal processing techniques such as power level difference ratio calculations and basic filtering operations that eliminate the need for resource-intensive computational models while maintaining effective noise suppression.
Solution Approach 2:
The patent replaces expensive, complex computational models with simple, computationally inexpensive operations. The system uses basic arithmetic operations (power level differences, ratios) and straightforward filtering techniques that require minimal processing resources, making the system suitable for low-power and low-cost applications without relying on sophisticated statistical models.
3Measurement precision
If complex mechanisms are used to distinguish sound from source of interest, then sound identification capability is improved, but processing time and computational load increase
Solution Approach 1:
The patent extracts and removes the complex statistical models and machine learning components from the voice activity detection system. Instead of using sophisticated algorithms, the invention employs simple signal processing techniques such as power level difference ratio calculations and basic filtering operations that eliminate the need for resource-intensive computational models while maintaining effective noise suppression.
Solution Approach 2:
The patent changes the approach from complex statistical parameter analysis to simple power level parameter comparisons. By focusing on fundamental acoustic parameters (power levels, frequency differences) rather than complex statistical features, the system achieves rapid processing with minimal computational overhead while maintaining effective voice activity detection capability.
Data Source
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AI summary
Methods and systems for identifying sound from a source of interest are provided for herein. In some embodiments, a first audio feed is captured by a first microphone and a second audio feed is captured by a second microphone. The first microphone may be located closer in proximity to the source of interest than the second microphone. The first audio feed can be processed utilizing the second audio feed to produce a first processed audio feed that can enable identification of sound originating from the source of interest. In some embodiments, the second audio feed can be additionally processed utilizing the first audio feed to produce a second processed audio feed. In such embodiments, frequencies from the first processed audio feed can be compared against frequencies of the second processed audio feed to identify sound originating from the source of interest. Other embodiments may be described and/or claimed herein.