Drone Sound Localization Modeling for Multi-Room False Trigger Reduction
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Solution Overview
Problem
Existing alarm and home automation systems face challenges in accurately localizing sound sources within buildings due to sound travel through conduits and varying acoustic properties, leading to false triggers and reduced efficiency.
Innovation Solution
A network of sound sensor devices combined with an autonomous device, such as a drone, to generate a sound localization model by emitting sound patterns and collecting data, allowing the system to identify the origin of sounds and trigger appropriate actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sound sensor devices are placed throughout the building to detect sound sources, then the system can trigger appropriate actions based on sound detection, but sound may travel through conduits and walls causing false triggers in wrong locations
Solution Approach 1:
The system performs preliminary sound localization modeling during a configuration phase before actual operation. A drone or robotic device systematically moves through the building to collect acoustic data and establish baseline sound propagation patterns. This preliminary action creates a reference model that enables accurate sound source localization during normal operation, preventing false triggers by understanding how sound travels through the specific building structure.
Solution Approach 2:
The patent replaces traditional mechanical sound localization methods (simple sensor proximity detection) with acoustic modeling and signal processing. Instead of relying on basic distance-based assumptions, the system uses computational acoustic models that account for sound propagation through walls, conduits, and different materials. This substitution of mechanical reasoning with acoustic physics-based modeling significantly improves localization accuracy.
2Measurement precision
If traditional sound detection methods are used without localization modeling, then the system is simpler to implement, but the system cannot accurately determine the origin of sounds
Solution Approach 1:
The system performs self-characterization by automatically collecting acoustic data and generating localization models without requiring manual input or expert configuration. The drone autonomously navigates the building, emits test sounds, and records sensor responses. The system then automatically processes this data to create the acoustic model, eliminating the need for manual setup while achieving high measurement precision.
Solution Approach 2:
The system changes physical parameters during the modeling process by having the drone move to different locations and emit sounds at various frequencies and intensities. This systematic variation of acoustic parameters allows the system to collect comprehensive data about sound propagation throughout the building, enabling accurate localization without requiring complex manual configuration.
3Productivity
If manual configuration methods are used for sound localization, then the process requires human intervention, but it can be time-consuming and less accurate
Solution Approach 1:
The system transitions from static manual configuration to dynamic automated data collection. The drone dynamically moves through the building space, systematically varying its position and emitting sounds adaptively. This dynamic approach allows rapid collection of comprehensive acoustic data from multiple perspectives, improving both the speed of model generation and the precision of sound localization compared to static manual methods.
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
The solution increases the accuracy of sound localization, reduces false notifications, and enhances the efficiency of event triggering by precisely determining the source of sounds within the building.
Implementation Method 1
Sound may travel within a building due to conduits such as air vents, ducts, and pipes, differences in wall thickness, and acoustic properties of each room
Data Source
AI summary
Techniques and systems are described for generating and using a sound localization model. A described technique includes obtaining for a building a sound sensor map indicating locations of first and second sound sensor devices in respective first and second rooms of the building; causing an autonomous device to navigate to the first room and to emit, during a time window, sound patterns at one or more frequencies within the first room; receiving sound data including first and second sound data respectively from the first and second sound sensor devices that are observed during the time window; and generating and storing a sound localization model based on the sound sensor map, autonomous device location information, and the received sound data, the model being configured to compensate for how sounds travels among rooms in at least a portion of the building such that an origin room of a sound source is identifiable.