Building Audio Tracking With Background Noise Filtering
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Solution Overview
Problem
Modern building management systems face challenges in reliably identifying human activities within buildings due to dominant background noise from HVAC and other equipment, making it difficult to distinguish human-related sounds.
Innovation Solution
An automated sound profiling system is used to capture and filter background noise profiles in the absence of humans, allowing for the identification of human activity by comparing real-time audio with sound classification models, and generating alerts for abnormal sounds or absence of expected sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If background noise filtering is not applied, then all audio signals are captured including HVAC equipment sounds, but human activity sounds cannot be distinguished from background noise
Solution Approach 1:
The system performs preliminary action by capturing background audio during calibration mode when no humans are present, generating background noise filters before actual human activity monitoring begins. These pre-generated filters are then applied during operational mode to remove HVAC and equipment noise, enabling accurate detection of human activity sounds.
2Measurement precision
If a calibration mode is implemented to capture background noise profiles, then background noise can be effectively filtered, but system complexity increases
Solution Approach 1:
The system implements dynamics by transitioning between two operational modes: calibration mode for capturing background noise profiles and generating filters, and operational mode for applying these filters to detect human activities. This dynamic mode switching allows the system to adapt its behavior based on operational requirements while maintaining manageable complexity through clear state separation.
3Productivity
If real-time audio is continuously analyzed without filtering, then all sounds are captured, but processing efficiency decreases and human activity detection accuracy is reduced
Solution Approach 1:
The system applies the extraction principle by removing background noise components from real-time audio signals using pre-generated background noise filters. This extraction of unwanted HVAC and equipment sounds from the audio stream improves the signal-to-noise ratio, enabling more efficient processing and accurate detection of human activity sounds.
Data Source
AI summary
Methods and systems for identifying human activity in a building. An illustrative method includes storing one or more room sound profiles for a room in a building based at least in part on background audio captured in the room without a presence of humans in the room. Background noise filters are generated for the room based on the room sound profiles. Real time audio may be captured from the room and filtered with at least one of the background noise filters. The filtered real time audio may be analyzed to identify one or more sounds associated with human activity in the room. A situation report may be generated based at least in part on the identified one or more sounds associated with human activity in the room and transmitted for use by a user.