Lighting Network Audio Sensing for Localized Event Detection
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Solution Overview
Problem
Existing lighting fixtures primarily focus on measuring environmental factors related to light output and do not effectively utilize networking circuitry for detecting and identifying auditory events, limiting their functionality and potential applications.
Innovation Solution
Incorporating audio sensors and machine learning algorithms, such as convolutional neural networks, into lighting fixtures to detect and identify auditory events like air leaks, high noise, or voice commands, enabling actions like notifications or sound cancellation, and utilizing multiple sensors to localize these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If lighting fixtures incorporate audio sensors and machine learning algorithms to detect auditory events, then the functionality and environmental awareness of lighting networks are enhanced, but the device complexity and manufacturing cost increase
Solution Approach 1:
The patent applies multi-functionality by enabling lighting fixtures to perform both their primary lighting function and secondary auditory detection functions. The audio sensors and machine learning algorithms allow the same device to detect various auditory events (air leaks, glass breaking, voice commands, etc.) while maintaining its lighting capabilities, thereby enhancing versatility without requiring entirely separate systems
Solution Approach 2:
The patent combines audio sensing capabilities, machine learning processing, and lighting control into a single integrated system. The audio sensor module is merged with the lighting fixture's existing processing circuitry and networking capabilities, allowing auditory event detection to be seamlessly integrated with lighting control functions in the same device
2Measurement precision
If multiple audio sensors are used to localize auditory events, then the precision of event localization is improved, but the device complexity and network infrastructure requirements increase
Solution Approach 1:
The patent divides the auditory detection task across multiple audio sensor modules distributed throughout the lighting network. Each sensor independently detects and processes auditory events in its local area, then results are aggregated through the existing mesh network to achieve comprehensive spatial localization without requiring a centralized complex processing system
Solution Approach 2:
The system implements feedback mechanisms where detected auditory events are communicated back through the networking circuitry to central control or other network nodes. This allows the network to collectively process localization data from multiple sensors, improving precision while utilizing the existing feedback-capable mesh network infrastructure
3Adaptability or versatility
If lighting fixtures are equipped with audio sensors for detecting auditory events, then the environmental awareness and user interaction capabilities are enhanced, but the manufacturing cost and ease of manufacture decrease
Solution Approach 1:
The patent leverages the existing mesh network infrastructure and processing capabilities of modern lighting fixtures to accommodate additional audio sensing functions. By designing the audio sensor module to integrate with standard lighting fixture architectures and utilize existing networking protocols, the system enhances environmental awareness while minimizing disruptions to manufacturing processes
Solution Approach 2:
The machine learning algorithms are designed to operate autonomously on the lighting fixture's existing processing circuitry, enabling the system to self-configure and self-calibrate audio detection parameters without requiring complex external setup or specialized manufacturing processes. The system automatically adapts to its environment and optimizes detection parameters
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
Enhances the functionality of lighting networks by allowing detection and identification of auditory events, enabling localized actions and improved network intelligence through sensor fusion, thereby providing enhanced user interaction and environmental awareness.
Implementation Method 1
measuring an auditory event through at least one audio sensor of a distributed network of audio sensors
Data Source
AI summary
Detection and identification of auditory events in distributed lighting networks is provided. Lighting fixtures or other devices in a distributed lighting network can incorporate an audio sensor (e.g., a microphone) through which auditory events (e.g., air leaks in compressed air systems, high noise events, shots fired, clapping, voice commands, etc.) are detected and measured. Through machine learning (e.g., a convolutional neural network), a type of auditory event can be identified, and action can be taken based on the type of the auditory event, such as to provide notification, alert nearby users, log events, provide sound cancelation, and so on. In some examples, the auditory event can be localized using multiple audio sensors. In some examples, a learning algorithm can fuse information from multiple sensor inputs, such as temperature sensors, cameras, occupancy sensors, light sensors, and so on.