Neural Network Loss Function for Audio Quantization

The patent replaces the traditional one-dimensional psychoacoustic model (mechanical system) with a neural network-based quantization strategy. The neural network classifier learns optimal quantization decisions from training data, substituting the fixed mathematical model with an adaptive intelligent system that can capture complex audio characteristics beyond the capabilities of conventional psychoacoustic models.

The patent changes the fundamental parameter of quantization strategy from fixed psychoacoustic thresholds to dynamic neural network predictions. By training the neural network on large datasets, the system learns to adapt quantization parameters based on local audio characteristics, enabling flexible parameter adjustment that the rigid one-dimensional model cannot achieve.

The patent applies partial action by using the neural network only for critical quantization decisions rather than processing the entire audio signal uniformly. The system focuses computational resources on determining optimal quantization indices where accuracy matters most, while leveraging the trained network's efficiency to reduce overall computational burden compared to exhaustive search methods.

The patent implements preliminary action through offline training of the neural network classifier. The computationally intensive training phase is performed in advance using large datasets, creating a compact model that can be deployed in real-time applications. This shifts the computational complexity from runtime operation to preprocessing, enabling accurate quantization with minimal real-time computational requirements.