Music Visualization Controller Using Neural Network Perception

The patent replaces the human controller (mechanical/biological system) with an automated controller that uses neural networks and machine learning algorithms to perceive and respond to music. The controller processes audio signals through multiple neural network layers to extract musical features, generate affect parameters, and control visual displays without human intervention, thereby eliminating the need for expensive human operators while maintaining accurate musical perception.

The patent creates a virtual copy of human musical perception capabilities through neural networks. The neural network models replicate the cognitive processes humans use to perceive music, including pitch detection, rhythm recognition, harmony analysis, and emotional interpretation. This digital copy can process music in real-time without the physical and economic constraints of human operators.

The patent replaces simple linear mathematical mappings with complex neural network processing. The neural networks perform non-linear transformations of audio signals to extract meaningful musical patterns and emotional content that linear mappings cannot capture. This substitution enables the system to handle complex musical structures while maintaining computational efficiency through optimized network architectures.

The patent transforms the representation of musical data from simple linear parameters (amplitude, frequency) to complex high-dimensional feature spaces through neural network processing. The system extracts multiple layers of musical features including timbre, rhythm, harmony, and affective parameters, creating a rich representation that captures human perception while remaining computationally manageable through efficient parameter transformations.

The patent performs preliminary learning and adaptation during an initial phase where the neural network analyzes and stores patterns from various musical genres and styles. This pre-learning enables the system to automatically generate appropriate visual responses for new music without requiring manual reprogramming. The controller adapts to different musical pieces in real-time using the pre-learned patterns, eliminating the need for time-consuming custom programming for each performance.

The patent implements a self-service system where the neural network automatically analyzes incoming audio signals and generates appropriate visual display control signals without human intervention. The system self-adjusts to different musical styles, genres, and emotional tones by processing the audio through its neural networks and directly controlling the visual displays, eliminating the need for human programmers to create custom responses for each musical piece.