Analog-to-Information Voice Recognition Sensor
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Solution Overview
Problem
Current voice recognition systems in low power wireless sensor networks face high power consumption and privacy concerns due to the need for continuous digital signal processing, especially in acoustic sensors, which are inefficient and threaten user privacy by reconstructing raw data.
Innovation Solution
Implementing an analog-to-information (A2I) process that extracts sparse sound features directly from analog signals using ultra-low power analog or mixed signal circuitry, reducing the need for high-resolution digital conversion and allowing continuous operation while minimizing power usage and maintaining user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital signal processing is used for continuous voice recognition, then recognition accuracy is improved, but power consumption increases significantly
Solution Approach 1:
The patent extracts only the essential features of voice signals (pitch, volume, duration, pauses) rather than processing the entire digital audio stream. This selective extraction of critical information maintains recognition accuracy while dramatically reducing computational load and power consumption in wireless sensor networks.
Solution Approach 2:
The patent changes the processing parameters by working with simplified acoustic features (pitch frequency, volume levels, temporal patterns) instead of full-resolution digital audio data. This parameter transformation enables effective voice recognition with minimal computational resources and power consumption.
2Measurement precision
If high-resolution digital conversion is implemented, then signal processing capability is improved, but power consumption and privacy risks increase
Solution Approach 1:
The patent extracts only essential acoustic features (pitch, volume, timing) from the analog signal and discards all other information. This selective extraction maintains voice command recognition capability while preventing reconstruction of the original speech content, thereby eliminating privacy threats.
Solution Approach 2:
The patent uses low-resolution feature representations that are sufficient for recognition but contain no recoverable personal information. These simplified digital features act as disposable data that fulfill the recognition function without posing privacy risks.
3Use of energy by moving object
If analog feature extraction is used, then power consumption is reduced, but processing complexity increases
Solution Approach 1:
The patent segments the voice signal analysis into distinct analog feature extraction stages (pitch detection, volume measurement, temporal pattern recognition) that can be implemented with simple dedicated circuits. This segmentation reduces overall system complexity while enabling low-power operation.
Solution Approach 2:
The patent replaces complex digital signal processing with simpler analog circuitry for feature extraction. By using analog methods to extract pitch, volume, and timing features directly from the input signal, the system reduces computational complexity and power consumption.
Data Source
AI summary
A low power sound recognition sensor is configured to receive an analog signal that may contain a signature sound. Sparse sound parameter information is extracted from the analog signal. The extracted sparse sound parameter information is processed using a speaker dependent sound signature database stored in the sound recognition sensor to identify sounds or speech contained in the analog signal. The sound signature database may include several user enrollments for a sound command each representing an entire word or multiword phrase. The extracted sparse sound parameter information may be compared to the multiple user enrolled signatures using cosine distance, Euclidean distance, correlation distance, etc., for example.