Personalized Audio Processing Models via Server-Based Feedback Updates
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Solution Overview
Problem
Audio devices lack sufficient computing power to effectively train AI models that reflect individual user usage patterns, leading to reduced effectiveness and inability to improve over time.
Innovation Solution
An initial audio processing model is provided to user devices, updated using user data collected locally and remotely, generating customized models tailored to individual user environments and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are trained in advance on generic data, then the audio device can operate with limited local computing power, but the model effectiveness is reduced because training data does not reflect individual user usage patterns
Solution Approach 1:
An initial audio processing model is trained in advance on generic training data and deployed to the audio device. This preliminary training enables the device to function with limited local computing power while still providing baseline audio processing capabilities. The pre-trained model serves as a foundation that can later be refined with user-specific data.
Solution Approach 2:
The system collects user data from the audio device during actual usage, sends this data to a server, and uses it to retrain and update the audio processing model. The updated model is then provided back to the user's device. This feedback loop enables continuous improvement of model effectiveness while adapting to individual user patterns without requiring extensive local computing resources.
2Device complexity
If the AI model is made static for deployment on small audio devices, then the device complexity is reduced, but the model cannot be improved over time
Solution Approach 1:
The model training and update functionality is extracted from the small audio device and relocated to an external server with sufficient computing power. The audio device retains only the inference capability, keeping its complexity low. The server handles the computationally intensive tasks of collecting user data, retraining models, and generating updates, which are then deployed back to the device.
Solution Approach 2:
A server acts as an intermediary between the user's audio device and the model training process. The server receives user data from the device, performs model retraining with enhanced computing resources, and provides updated models back to the device. This intermediary enables continuous model improvement while the audio device itself remains simple and static in structure.
Data Source
AI summary
A method of providing an audio processing model is provided. The method comprises providing, over a network, an initial audio processing model to a first user device and a second user device; receiving first user data from the first user device and second user data from the second user device; updating the initial audio processing model to generate a first updated audio processing model based on the first user data; updating the initial audio processing model to generate a second updated audio processing model based on the second user data; and providing, over the network, the first updated audio processing model to the first user device and the second updated audio processing model to the second user device. Related methods and devices are also provided.