Analog-to-Information Sound Sensor for Low Power Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital sound recognition systems consume high power due to continuous operation and require high-performance analog-front-end (AFE) and analog-to-digital converter (ADC) components, which also pose privacy risks by reconstructing raw input signals.
Innovation Solution
Implementing an analog-to-information (A2I) system that extracts sparse sound features directly from analog signals using ultra-low power analog or mixed signal circuitry, reducing the need for continuous digital sampling and processing, and employing a sigma-delta ADC for low power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous digital sampling and processing is used for sound recognition, then recognition accuracy is improved, but power consumption increases significantly
Solution Approach 1:
The patent extracts only the essential sound features (such as frequency spectrum, temporal envelope, and spectral characteristics) from the analog signal using analog front-end circuitry, rather than converting and processing the entire digital signal. This extraction approach maintains recognition accuracy while dramatically reducing the data volume requiring digital processing, thereby lowering power consumption.
Solution Approach 2:
The patent replaces digital signal processing operations with analog circuit implementations for feature extraction functions. Analog filters, envelope detectors, and spectral analyzers perform feature extraction directly in the analog domain, eliminating the need for high-power analog-to-digital conversion and digital computation, thus resolving the power-accuracy tradeoff.
2Measurement precision
If high-performance ADC and AFE components are used, then sound recognition quality is improved, but device complexity and power consumption increase
Solution Approach 1:
The system extracts only the necessary acoustic features (frequency content, temporal patterns, spectral characteristics) using simplified analog circuitry before digital conversion. This feature extraction approach maintains recognition quality while reducing the requirements for ADC performance and overall system complexity.
Solution Approach 2:
The patent transforms the sound signal from its raw waveform representation into a compressed feature space containing only the relevant acoustic parameters. This parameter transformation allows lower-performance, less complex ADC and processing components to achieve the same recognition quality that would require high-performance components operating on raw signals.
3Reliability
If continuous operation is maintained for event detection, then event detection reliability is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic wake-up cycles where the low-power analog front-end continuously monitors for trigger events, and only activates the higher-power digital processing components when an event is detected. This periodic operation maintains detection reliability by ensuring the system is always monitoring, while dramatically reducing average power consumption through duty-cycled processing.
Solution Approach 2:
The analog front-end circuitry serves as an intermediary between the continuous analog signal and the intermittent digital processing. It continuously conditions and pre-processes the signal in low-power analog mode, then triggers digital conversion and processing only when relevant events occur, thereby maintaining reliability while reducing overall 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 sound parameter information is sampled in a periodic manner and a context value is updated to indicate a current environmental condition. The sparse sound parameter information is compared to both the context value and a signature sound parameter database stored locally with the sound recognition sensor to identify sounds or speech contained in the analog signal, such that identification of sound or speech is adaptive to the current environmental condition.