Audio Keyword Control for Low-Power Interface Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computational limitations in devices such as smartphones lead to inefficient navigation and resource drainage due to intensive user interface interactions, which can cause lag and battery drain.
Innovation Solution
Implementing an audio control system with machine learning schemes, specifically neural networks, to detect keywords and perform actions on the device, such as navigating user interfaces and selecting elements, thereby reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are used to recognize different keywords, then the functionality and versatility of the audio control system is improved, but the device complexity and computational resources required increase
Solution Approach 1:
A single machine learning model is designed to perform multiple functions by recognizing different keywords (e.g., 'first keyword', 'second keyword') and triggering different actions. This universal model replaces the need for separate specialized models for each keyword, thereby maintaining versatility while reducing device complexity and computational overhead.
2Ease of operation
If audio-based control is implemented, then the ease of operation is improved, but the reliability may deteriorate due to misrecognition of keywords
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from user interactions and audio inputs. The model can be retrained with new audio data captured from the user, allowing it to adapt to individual speech patterns and improve recognition accuracy over time, thereby enhancing reliability while maintaining ease of operation.
3Speed
If continuous audio monitoring is performed to detect keywords, then the responsiveness of the system is improved, but the energy consumption increases
Solution Approach 1:
Instead of continuous audio monitoring, the system uses periodic or event-driven audio processing. The machine learning model is activated at specific intervals or triggered by wake words/sleep words, allowing the device to enter low-power states between activations. This approach maintains responsive keyword detection while significantly reducing overall energy consumption compared to continuous monitoring.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An audio control system can control interactions with an application or device using keywords spoken by a user of the device. The audio control system can use machine learning models (e.g., a neural network model) trained to recognize one or more keywords. Which machine learning model is activated can depend on the active location in the application or device. Responsive to detecting keywords, different actions are performed by the device, such as navigation to a pre-specified area of the application.