IoT Lighting Microphone Speaker Selection for Occupancy Detection
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Solution Overview
Problem
Existing connected lighting IoT systems face inefficiencies in evaluating characteristics like occupancy and people count due to the overwhelming computational load when using multiple microphones and speakers, necessitating a method to select a subset of microphones and speakers for efficient evaluation.
Innovation Solution
A connected lighting system that associates microphones with specific areas, determines a baseline channel matrix during commissioning, and selects combinations of microphones and speakers to efficiently evaluate characteristics by analyzing audio signals and channel responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every microphone and speaker is used to evaluate building space characteristics, then measurement precision is improved, but device complexity and computational load increase
Solution Approach 1:
The system divides the building space into multiple detection zones, each associated with a specific subset of microphones and speakers. During commissioning, the system determines which audio channels are relevant for each zone and creates a baseline channel matrix for that subset. This segmentation allows the system to process only the necessary channels for each zone independently, reducing overall computational complexity while maintaining measurement precision for each specific area.
Solution Approach 2:
Different subsets of microphones and speakers are selected based on their spatial relationship with specific detection zones. The system determines local quality by evaluating which audio channels provide the most relevant information for each zone, using the baseline channel matrix to identify optimal channel combinations. This ensures that each zone uses only the locally relevant channels rather than processing all channels uniformly.
2Measurement precision
If every microphone and speaker is used to evaluate building space characteristics, then measurement precision is improved, but computational load increases
Solution Approach 1:
The system segments the audio channels into zone-specific subsets based on spatial relationships during commissioning. Each detection zone has an associated baseline channel matrix that identifies the relevant microphones and speakers for that zone. During operation, the system processes only the channels corresponding to the active detection zone, significantly reducing computational load while maintaining detection accuracy for that specific area.
Solution Approach 2:
Instead of processing all available audio channels, the system uses only the necessary subset of channels for each detection zone. The baseline channel matrix identifies which channels are excessive (not needed) for a given zone, allowing the system to perform partial action by processing only essential channels, thereby reducing computational requirements while maintaining sufficient measurement precision.
3Power
If a subset of microphones and speakers is selected, then computational load is reduced, but measurement precision may deteriorate
Solution Approach 1:
During the commissioning process, the system performs preliminary actions by determining the baseline channel matrix for each detection zone. This baseline includes information about the spatial relationships and acoustic characteristics of the selected microphones and speakers. By storing this baseline information in advance, the system can quickly compare current channel responses against the baseline during operation, maintaining measurement precision without requiring processing of all channels.
Solution Approach 2:
The system uses the baseline channel matrix as a reference for feedback comparison. During operation, the system compares the current channel response matrix against the stored baseline to detect changes in occupancy status. This feedback mechanism allows the system to maintain high measurement precision by detecting subtle changes in the acoustic field, even when using only a subset of channels, because the baseline provides a detailed reference for what the channel response should be under different occupancy conditions.
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
This approach allows for efficient evaluation of occupancy, people count, and other characteristics by focusing on the most relevant audio channels, reducing computational load and improving processing efficiency.
Implementation Method 1
The selected speakers are configured to generate a plurality of audio signals based on the command signal
Implementation Method 2
Each of the activated microphones corresponds to at least one of the pairs associated with one or more of the selected detection areas
Implementation Method 3
Each pair forms one of a plurality of audio multipath transmission channels
Data Source
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AI summary
A system for evaluating a characteristic of a portion of a building space, such as a room, may be provided. The system includes pairs of speakers and microphones associated with detection areas and forming audio multipath transmission channels. The system includes a controller communicatively coupled to the speakers and the microphones. The controller is configured to: (1) select detection areas; (2) activate the microphones corresponding to the pairs associated with the selected detection areas to capture one or more audio samples; (3) select speakers based on a baseline channel response matrix and the activated microphones; (4) transmit command signals to the selected speakers; (5) determine a characteristic channel response matrix based on the audio samples and audio signals corresponding to the command signals; and (6) evaluate the characteristic of the portion of the building space based on the characteristic channel response matrix and the baseline channel response matrix.