Abstract: Using generative deep learning models for signal processing of sound waves is the focus of the talk . A new network design is developed to learn the transfer function of a loudspeaker. The transfer function of loudspeakers is learned by a deep generative network using training pairs from the ground truth data and the recorded (simulated) data from the output of the loudspeaker. This network is used to train a second generative network to learn the inverse transfer function of the same loudspeaker and is used to pre-distort the sound wave signal in order to get the optimal performance from the loudspeaker. Caffe-framework with python interface is used to develop and implement the generative deep learning models.