US Patent 11853884 Many or one detection classification systems and methods

A classification training system comprises a neural network configured to perform classification of input data, a training dataset including pre-segmented, labeled training samples, and a classification training module configured to train the neural network using the training dataset. The classification training module includes a forward pass processing module, and a backward pass processing module. The backward pass processing module is configured to determine whether a current frame is in a region of target (ROT), determine ROT information such as beginning and length of the ROT and update weights and biases using a cross-entropy cost function and a tunable many-or-one detection (MOOD) cost function, that comprises a tunable hyperparameter for tuning the classifier for a particular task. The backward pass module further computes a soft target value using ROT information and computes a signal output error using the soft target value and network output value.