Voice Device Clustering via Timestamp and SNR Metadata
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Solution Overview
Problem
The proliferation of multiple voice-enabled devices in an environment increases complexity in determining which device should respond to a voice command, especially in noisy conditions or when multiple devices detect the same command, leading to confusion and incorrect responses.
Innovation Solution
The use of contextual information such as timestamp data and signal-to-noise (SNR) values to cluster voice-enabled devices, allowing for the selection of the most appropriate device to respond to a voice command by analyzing metadata and audio data from multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple voice-enabled devices are placed throughout an environment, then user convenience and coverage are improved, but device complexity and difficulty in determining which device should respond increase
Solution Approach 1:
The system segments the environment into multiple device clusters based on spatial proximity and acoustic characteristics. Each cluster represents a localized group of devices that can independently handle voice commands, reducing the overall system complexity by dividing the decision-making process into smaller, manageable segments rather than requiring global coordination across all devices.
Solution Approach 2:
The patent applies local quality by making each device's response capability context-dependent on its local environment. Devices use local acoustic metrics (SNR, timestamp) and spatial information to determine their suitability for responding to voice commands, rather than relying on a centralized arbitration system. This distributes the intelligence and reduces system-wide complexity.
2Reliability
If multiple voice-enabled devices detect the same voice command, then reliability of command detection is improved, but correctness of response selection deteriorates due to confusion
Solution Approach 1:
The system uses feedback from acoustic metrics (signal-to-noise ratio, timestamp) and spatial information to continuously refine device cluster assignments and response selection. Devices provide feedback about their detection quality and environmental conditions, allowing the system to dynamically adjust which device should respond based on real-time performance metrics rather than static configurations.
Solution Approach 2:
The patent changes parameters such as cluster assignment thresholds, SNR requirements, and temporal windows based on environmental conditions and device performance. By dynamically adjusting these parameters, the system maintains high detection reliability while improving response selection precision adaptively rather than relying on fixed thresholds.
3Measurement precision
If contextual information such as timestamp data and SNR values is used to cluster devices, then response accuracy is improved, but processing complexity and time increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing device clusters based on spatial and acoustic characteristics before voice commands are issued. This clustering information is maintained and updated periodically, so when a voice command is detected, the system can quickly determine which pre-formed cluster should handle the command rather than analyzing all devices from scratch, significantly reducing processing time.
Solution Approach 2:
Instead of analyzing contextual information from all devices in the environment, the system applies partial action by focusing only on devices within the relevant device cluster. This selective processing of contextual data (timestamp, SNR) for a subset of devices rather than the entire device population reduces computational overhead while maintaining selection accuracy.
Data Source
AI summary
This disclosure describes, in part, techniques for determining device groupings, or clusters, for multiple voice-enabled devices. The device clusters may be determined based on metadata data for audio signals (or audio data) generated by each of the multiple voice-enabled devices. For example, a remote system may analyze timestamp data for the audio signals received from the devices, and determine that the devices detected the same voice command of a user based on the timestamp data indicating that the audio signals were received within a threshold period of time from each other. Additionally, the remote system may analyze other metadata of the audio data, such as signal-to-noise (SNR) values, and determine that the SNR values are within a threshold value. The remote system may determine device clusters for the voice-enabled devices of a user based on these, and potentially other, types of metadata of the audio signals.