Neural Network Audio Training With User Preference Loss Functions
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Solution Overview
Problem
Existing audio processing technologies using neural networks struggle to adapt to varying user environments and preferences, leading to suboptimal performance in removing unwanted sound components, as they are typically trained for specific environments and preferences, limiting their versatility and user satisfaction.
Innovation Solution
A method for training neural networks using pairs of target and distorted audio data sets in conjunction with user preference settings, allowing the network to be fine-tuned for different preferences and environments, enabling it to meet a variety of user needs with a single trained model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network is trained for a specific environment and user preference, then the audio processing performance for that specific case is improved, but the adaptability to other environments and preferences deteriorates
Solution Approach 1:
The patent makes the loss function universal by incorporating it as an input to the neural network alongside audio data. This allows a single trained network to serve multiple functions - processing audio for different environments and user preferences without requiring separate networks for each scenario. The loss function acts as a configurable parameter that adapts the same network structure to different use cases.
Solution Approach 2:
The patent introduces dynamics by making the loss function variable rather than fixed. During inference, the loss function can be dynamically adjusted based on the desired user preference and environment, allowing the neural network to adapt its behavior in real-time. This transforms a static processing system into a dynamic one that responds to changing requirements.
2Adaptability or versatility
If multiple neural networks are trained for different environments and preferences, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
Instead of deploying multiple specialized neural networks, the patent creates a single universal network that can handle different environments and preferences through configurable loss functions. This reduces device complexity by consolidating what would otherwise require multiple separate models into one adaptable system.
Solution Approach 2:
The patent changes the approach from modifying network structure (multiple networks) to modifying network behavior through parameter changes (loss function configuration). By adjusting the loss function parameters rather than deploying different network architectures, the system achieves adaptability with minimal increase in complexity.
3Measurement precision
If the training data does not correspond to the actual sound environment, then the speech intelligibility is improved for the training environment, but the performance in actual usage environment deteriorates
Solution Approach 1:
The patent performs preliminary configuration of the loss function to match expected usage scenarios. By pre-defining appropriate loss functions for different environments and preferences, the system prepares in advance for various actual usage conditions, ensuring optimal performance regardless of where or how the device is ultimately used.
Solution Approach 2:
The system dynamically selects or adjusts the loss function based on the actual usage environment detected during operation. This allows the neural network to adapt its processing characteristics to match the current environment, bridging the gap between training conditions and real-world usage scenarios.
Data Source
AI summary
A computer-implemented method (600) for training a neural network (100) by using a data processing device (102) is presented. The method (600) comprises providing (602) pairs (110) of target audio data sets (106) and distorted audio data sets (108) to the neural network (100), providing (604) values (112) of user preference settings, wherein the values (112) are associated to the pairs (110) of target and distorted audio data sets (106, 108), such that the neural network (100) can be trained by a combination of the pairs (110) of the target audio data (106) and the distorted audio data (108) in combination with the values (112) of the user preference settings, wherein the distorted audio data (108) and the values (112) of the user preference settings are used as inputs to the neural network (100) during training, and wherein a loss function (114) used for training the neural network is a function of the values (112) of the user preference settings, the target audio data (106), and the distorted audio data (108) thereby providing for that a variety of user preference settings can be met with one and the same trained neural network.