Audio-Based Human Activity Detection Using Background Noise Profiling
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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 occupancy anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If background noise from HVAC and equipment is present in the building, then the building management system can operate normally, but human activity detection becomes unreliable due to dominant background noise
Solution Approach 1:
The system performs preliminary action by capturing background noise profiles during calibration phases when no humans are present, storing these profiles for later use. This advance preparation enables the system to subsequently filter out known background noises during human activity detection, resolving the contradiction between normal building operation and reliable detection.
Solution Approach 2:
The system extracts and separates background noise components from the overall audio signal by comparing real-time audio against stored background profiles. This extraction process isolates human activity sounds from the dominant HVAC and equipment noise, enabling reliable detection despite the presence of background interference.
2Measurement precision
If background noise filtering is implemented to improve human activity detection, then detection accuracy improves, but system complexity increases due to calibration and profile management requirements
Solution Approach 1:
The system implements self-service by automatically performing calibration procedures and generating background noise profiles without requiring manual intervention. The system autonomously captures audio during designated calibration periods, processes the data to create filters, and applies these filters during operation, reducing operational complexity while maintaining high detection precision.
Solution Approach 2:
The system merges the background noise filtering function with the existing building management system's audio processing capabilities. By integrating sound profile capture, storage, and filtering operations into the BMS infrastructure, the system achieves accurate human activity detection without proportionally increasing overall system complexity.
3Measurement precision
If real-time audio filtering with background noise profiles is performed, then human activity identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-computing and storing background noise profiles during calibration phases. These pre-processed profiles are then reused during real-time operation, avoiding the need to compute filters from raw audio data in real-time. This approach maintains high classification accuracy while significantly reducing processing time during active monitoring.
Solution Approach 2:
The system creates simplified copies of background noise characteristics in the form of stored profiles and filters. Instead of processing complete audio spectra in real-time, the system compares real-time audio against these condensed profile representations, maintaining detection accuracy while reducing computational burden and processing time.
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.