Multimodal Beamforming for User Prioritization in Noisy Environments
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Solution Overview
Problem
Current conversational agents struggle to filter out irrelevant input in noisy environments and prioritize users effectively, leading to false reactions and equal treatment of all users, without leveraging meta-information to focus on a targeted user.
Innovation Solution
A multimodal beamforming and attention filtering system that uses direction of arrival, video input, and meta-information to filter out irrelevant speech, prioritize engaged users, and adjust its mode based on interaction types, employing sensors like microphones, cameras, and radar to maintain a world map and focus on the primary user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current conversational agents use a single mode to receive input from every user, then all users are treated equally, but the system cannot filter out irrelevant input or prioritize targeted users in noisy environments
Solution Approach 1:
The system segments users into different priority levels based on engagement detection, separating the targeted user from other users in the environment. This allows the conversational agent to focus computational resources on the relevant user while filtering out irrelevant input from others.
Solution Approach 2:
The system dynamically adjusts its operational mode between single-user and multi-user configurations based on real-time detection of user engagement. This dynamic adaptation allows the system to optimize performance for the current situation while maintaining flexibility for different interaction scenarios.
2Reliability
If the system uses direction of arrival to improve audio input, then noise reduction is partially achieved, but the system lacks active optimization means to further reduce noise and focus on engaged users
Solution Approach 1:
The system introduces video input and sensor data as intermediary information sources that help identify and isolate the engaged user. These intermediaries provide additional cues beyond audio direction of arrival, enabling more precise noise filtering and focus on the relevant user.
Solution Approach 2:
The system changes operational parameters such as beamforming weights and attention filters based on detected user engagement characteristics. By dynamically adjusting these parameters according to video and sensor data, the system actively optimizes audio processing to reduce noise and enhance the targeted user's input.
3Ease of operation
If the conversational agent reacts to all wake words equally, then it responds to every user, but it produces false reactions when users accidentally address the agent
Solution Approach 1:
The system performs preliminary detection of user engagement through video and sensor input before responding to wake words. This preliminary action allows the system to pre-filter potential interactions and only respond when a user is genuinely engaged, preventing false reactions while maintaining accessibility for legitimate users.
Data Source
AI summary
Systems and methods for creating a view of an environment are disclosed. Exemplary implementations may: receive parameters and measurements from at least two of one or more microphones, one or more imaging devices, a radar sensor, a lidar sensor, and/or one or more infrared imaging devices located in a computing device; analyze the parameters and measurements received from the multimodal input; generate a world map of the environment around the computing device; and repeat the receiving of parameters and measurements from the input devices and the analyzing steps on a periodic basis to maintain a persistent world map of the environment.