Multi-Assistant Acoustic Event Detection With Cross-Device Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for detecting acoustic events in an ecosystem of assistant devices are prone to false positives, leading to unnecessary activation of automated assistant components and resource wastage.
Innovation Solution
Simultaneously process audio data from multiple assistant devices using event detection models to verify the occurrence of acoustic events, reducing false positives by ensuring temporally corresponding audio data is processed across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If acoustic event detection is performed at a single assistant device, then the system responds quickly to detected events, but false positive detection rate increases
Solution Approach 1:
The patent combines acoustic event detection results from multiple assistant devices by processing temporally corresponding audio data captured simultaneously at different devices. This merging approach cross-validates detections across devices, reducing false positives while maintaining individual device simplicity.
2Reliability
If multiple assistant devices process audio data simultaneously, then false positive detection is reduced, but computational resources are consumed
Solution Approach 1:
The system performs preliminary filtering by identifying temporally corresponding audio data segments across devices before full processing. Only audio data captured during the same time window is processed together, reducing the total computational load while maintaining detection reliability.
Solution Approach 2:
The patent extracts and processes only the relevant temporal portion of audio data from multiple devices - specifically the time windows where acoustic events are detected. This extraction approach avoids processing entire audio streams, reducing computational resource consumption.
3Reliability
If acoustic event detection is performed across multiple devices, then false positives are reduced, but network communication overhead increases
Solution Approach 1:
The system extracts and transmits only the essential detection information - temporal markers and audio data segments - from multiple devices rather than exchanging complete audio streams. This reduces network communication overhead while enabling cross-device validation.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
Implementations can detect respective audio data that captures an acoustic event at multiple assistant devices in an ecosystem that includes a plurality of assistant devices, process the respective audio data locally at each of the multiple assistant devices to generate respective measures that are associated with the acoustic event using respective event detection models, process the respective measures to determine whether the detected acoustic event is an actual acoustic event, and cause an action associated with the actional acoustic event to be performed in response to determining that the detected acoustic event is the actual acoustic event. In some implementations, the multiple assistant devices that detected the respective audio data are anticipated to detect the respective audio data that captures the actual acoustic event based on a plurality of historical acoustic events being detected at each of the multiple assistant devices.