Audio Tone Detection via Fingerprinting
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Solution Overview
Problem
Existing systems lack an efficient method to detect and interpret audio tones from devices in a property, such as beeps, which are crucial for determining device states and triggering appropriate actions, due to the complexity of distinguishing between various tones and their meanings.
Innovation Solution
A monitoring system that utilizes microphones to capture sound data, employs machine learning models to identify audio tones, generates audio fingerprints, and searches databases to determine device identifiers and states, allowing for notifications and operations based on detected tones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio detection methods are used to identify device tones, then the system can detect audio sounds, but it cannot accurately distinguish between different device tones and their meanings
Solution Approach 1:
The system transforms audio tones into audio fingerprints by extracting and encoding specific acoustic parameters (frequency, duration, pattern). This parameter transformation enables precise identification of device tones without requiring complex analysis of the entire audio signal, thus improving detection accuracy while managing system complexity.
Solution Approach 2:
The patent replaces traditional mechanical/audio signal analysis methods with machine learning models that process audio data. The machine learning component automatically learns to distinguish different device tones and their meanings, achieving high detection accuracy without manual programming of complex detection rules.
2Reliability
If the system continuously monitors all audio sounds to detect device tones, then detection coverage is maximized, but energy consumption increases
Solution Approach 1:
The system performs partial monitoring by focusing computational resources only on identifying audio tones that match known device patterns. Rather than analyzing every audio event in detail, the system uses audio fingerprints to quickly filter and identify relevant device tones, maintaining detection coverage while reducing energy consumption through selective processing.
3Measurement precision
If the system stores detailed audio data for analysis, then tone identification accuracy improves, but storage requirements increase
Solution Approach 1:
The system extracts only the essential characteristics of audio tones and stores them as compact audio fingerprints. By extracting key parameters (frequency patterns, duration, timing) and storing them in a condensed format rather than preserving complete audio recordings, the system maintains accurate tone identification capability while minimizing storage requirements.
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
Enables accurate detection and interpretation of audio tones from devices, facilitating timely notifications and actions, such as alerting users to device states or events, thereby enhancing property monitoring and management.
Implementation Method 1
obtain sound data of audio sounds detected by a microphone that is located at the property
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a storage device, for beep detection and interpretation is disclosed. In one aspect, a monitoring system is disclosed that includes a processor and a storage device storing instructions that, when executed by the processor, causes the processor to perform operations. The operations may include obtaining sound data of audio sounds detected by a microphone that is located at the property, determining whether the sound data includes data representing one or more audio tones generated by a device, based on determining that the obtained sound data includes one or more audio tones generated by a device, generating an audio fingerprint of the sound data, determining, using the generated audio fingerprint, a state of the device that generated the one or more audio tones, and performing an operation based on the state of the device that generated the one or more audio tones.