Acoustic Hazard Detection With Background-Noise Extraction
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Solution Overview
Problem
Existing fire detection systems often fail to detect fires at early stages and provide accurate location, direction, intensity, and speed of fire due to reliance on heat or smoke detection, which requires fire to reach dangerous levels and are limited by line of sight and airflow requirements.
Innovation Solution
A soundwave-based detection system using microphones to distinguish background noise from anomaly soundwaves, employing machine learning algorithms to identify fire, flame, spark, or ignition, and determine their location, direction, intensity, and speed, with integration to imaging devices for further monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heat or smoke detection is used, then fire can be detected, but detection occurs only after fire reaches dangerous levels
Solution Approach 1:
The patent replaces thermal detection mechanisms with acoustic detection. The system uses microphones to capture sound waves generated by fire combustion processes, enabling detection before thermal signatures become prominent. This substitution allows early-stage fire detection by listening to the acoustic signals produced during combustion rather than waiting for heat or smoke accumulation.
Solution Approach 2:
The patent introduces sound waves as an intermediary detection medium between the fire hazard and the detection system. By capturing acoustic signals that propagate from combustion sources, the system creates an alternative detection pathway that operates independently of thermal or particulate matter, enabling earlier warning signals before traditional detectors are triggered.
2Reliability
If heat or smoke detection is used, then fire can be detected, but detection is limited by line of sight and airflow requirements
Solution Approach 1:
The patent replaces optical and particulate-based detection with acoustic field detection. Sound waves can propagate through obstacles and different air currents without requiring direct line of sight or specific airflow patterns, enabling the system to detect fires in locations that are invisible or inaccessible to traditional thermal and smoke detectors.
Solution Approach 2:
The patent transitions from two-dimensional optical detection (requiring line of sight) to three-dimensional acoustic field detection. Sound waves radiate spherically from sources and can be detected from multiple positions and angles, providing omnidirectional coverage and the ability to detect fires through or around obstacles that block optical sensors.
3Loss of time
If soundwave-based detection is used, then early detection is enabled, but background noise interferes with detection accuracy
Solution Approach 1:
The patent extracts and isolates the acoustic signature of fire combustion from the complex background noise environment. By identifying and separating the characteristic frequency patterns and temporal characteristics of fire sounds from ambient noise, the system can detect fires accurately even in noisy settings, extracting the relevant hazard signal while rejecting irrelevant background interference.
Solution Approach 2:
The patent applies location-specific acoustic filtering and pattern recognition tailored to different environmental contexts. The system adapts its detection algorithms to the specific acoustic characteristics of each location, using local environmental knowledge to distinguish fire signatures from background noise patterns unique to each setting, thereby maintaining high accuracy across diverse environments.
4Measurement precision
If multiple soundwave analysis is performed, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex soundwave analysis task into separate functional modules: acoustic signal capture, feature extraction, pattern recognition, and hazard classification. Each module performs a specific function in the detection pipeline, making the overall system more manageable and maintainable while achieving high accuracy through coordinated operation of specialized components rather than monolithic complexity.
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
Enables early detection of fire hazards and precise determination of their characteristics, reducing false alarms and improving response time by utilizing soundwave analysis and machine learning for accurate hazard detection.
Implementation Method 1
a microphone configured to detect a combination of a first soundwave and a second soundwave
Data Source
AI summary
A detection apparatus is provided. The detection apparatus may include a microphone configured to detect a combination of a first and second soundwave, wherein the first soundwave comprises background noise, a memory configured to store a first pre-determined sound signature corresponding to the background noise and a second pre-determined sound signature corresponding to a hazard soundwave. The detection apparatus may include a controller component configured to identify a presence of the second soundwave in the detected combination by matching a signature of the combination with the first pre-determined sound signature, reduce a level of the first soundwave in the detected combination using a noise reduction, produce an extracted second soundwave based on the noise reduction, determine an extracted sound signature corresponding to the extracted second soundwave, determine a matching level of the extracted sound signature with the second pre-determined sound signature, and determine a presence of a known hazard.