Sound Pleasantness Prediction via Binary Classification

The system performs preliminary actions by collecting sound quality data and human ratings in advance, then training a machine learning model offline. This pre-training allows the model to make rapid predictions without requiring real-time human jury involvement, thus reducing evaluation time while maintaining precision through the model's learned patterns from extensive training data.

The invention creates a computational copy of human judgment through a trained machine learning model. The model learns to replicate human pleasantness ratings by training on data from human juries, then uses this learned knowledge to make predictions without requiring actual human listeners. This copying approach preserves measurement precision while eliminating time losses associated with human evaluation.

The machine learning model serves multiple functions across different products and sound types. A single trained model can evaluate pleasantness for various electronic devices, appliances, and sound categories without requiring separate human juries for each product. This universality provides adaptability to different products while eliminating the time required to assemble and train new juries for each evaluation.

The system performs preliminary training on diverse sound data from multiple products and categories before deployment. This pre-training creates a versatile model that can adapt to different product types without requiring product-specific human juries, thus maintaining adaptability while reducing evaluation time for each new product.

The system creates a computational model that copies human perception patterns by training on ratings from diverse human listeners. The model learns to account for individual perception differences and preferences, then applies this learned understanding to predict pleasantness for new sounds. This approach maintains the precision of controlled measurements while incorporating the variability and nuances of human perception that traditional NVH analysis misses.

The invention transforms traditional NVH parameters into a framework that incorporates human perception variables. By training the model on human ratings alongside objective sound measurements, the system adjusts and weights different parameters based on their actual impact on human perception, making the analysis more adaptable to how humans truly experience sound while maintaining measurement precision.