Tunable Band-Pass Filtering for Low-Power Audio Signal Detection
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Solution Overview
Problem
Battery-operated wireless sensor nodes face power consumption issues when processing audio signals for speech recognition and noise detection due to high power requirements, making it difficult to achieve efficient signal analysis with existing complex hardware structures.
Innovation Solution
A method using a bandpass filter with a controllable pass frequency range, allowing for efficient signal analysis by selecting specific spectral ranges for processing, reducing the need for complex hardware and enabling prolonged operation in simple sensor nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital data processing is used for speech recognition and noise detection, then signal analysis capability is improved, but power consumption increases significantly
Solution Approach 1:
The patent divides the signal processing task into two segments: a simple noise detection stage using minimal processing, and a more complex speech recognition stage activated only when noise is detected. This segmentation allows the system to maintain low average power consumption while preserving the capability for detailed signal analysis when needed.
Solution Approach 2:
The system employs periodic noise detection at intervals rather than continuous processing. The noise detection unit periodically analyzes the input signal to determine whether noise is present, and only activates the speech recognition processing when necessary. This periodic action significantly reduces average power consumption while maintaining effective signal analysis capability.
2Measurement precision
If complex hardware structures are used for signal processing, then signal analysis accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the signal processing function into two distinct units: a simple noise detection unit and a speech recognition unit. The noise detection unit uses minimal hardware to detect noise presence, and only when noise is detected does the system activate the more complex speech recognition processing. This segmentation achieves accurate signal analysis without requiring the entire complex hardware structure to be active continuously.
Solution Approach 2:
The system dynamically adjusts its processing complexity based on the detected signal conditions. When no noise is present, the simple noise detection pathway is sufficient. When noise is detected, the system dynamically activates the speech recognition unit to perform more complex analysis. This dynamic adaptation allows accurate signal analysis while avoiding unnecessary complexity in normal operating conditions.
3Measurement precision
If continuous signal processing is performed, then detection accuracy is improved, but operating time of battery-powered system decreases
Solution Approach 1:
The patent implements periodic noise detection rather than continuous processing. The noise detection unit operates at scheduled intervals to check for noise presence, and only activates speech recognition processing when noise is detected. This periodic action maintains detection accuracy for noise events while dramatically extending the operating time of battery-powered sensor nodes by reducing average power consumption.
Solution Approach 2:
The system segments processing into a always-active but simple noise detection stage and a conditionally-active speech recognition stage. The noise detection segment runs periodically with minimal power, while the speech recognition segment activates only when noise is detected. This segmentation ensures detection accuracy is maintained for noise events while extending operating time through reduced overall power consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient signal processing with reduced power consumption and complexity, enabling autonomous sensor nodes to detect speech patterns and noise over extended periods without requiring complex hardware structures.
Implementation Method 1
a bandpass filter with a controllable pass frequency range, allowing for efficient signal analysis by selecting specific spectral ranges for processing
Data Source
Figure 1
Figure 2A~2B
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AI summary
The invention relates to a method (400) for processing a signal (105). The method (400) has a step of reading (410) the signal (105) and a step of filtering (420) the signal (105, 105') using a band-pass filter (BPF1) in order to obtain a band-pass filter signal (130). The method (400) additionally has a step of an analog-digital conversion (430) of the band-pass filter signal (130) or a signal (130') derived from the band-pass filter signal (130) in order to obtain an analog-digital filter signal (135). The method (400) also has a step of evaluating (440) the analog-digital filter signal (135) using an expected value (E1) in order to obtain a control signal (145), wherein the expected value (E1) represents an expected signal parameter of the signal (105). Finally, the method (400) has a step of actuating (450) the band-pass filter (BPF1) depending on the control signal (145) in order to change a pass band frequency range of the band-pass filter (BPF1) from a first spectral pass band to a second spectral pass band.