Morphing dynamical sound models

We investigate a new approach to sound morphing that is strictly in the time domain. It is based upon a method of modeling and synthesizing natural sounds with neural networks that has been introduced previously and is called dynamic or attractor modeling. By means of basic synthetic signals we investigate the fundamental properties of the proposed morphing scheme. Using two real world sound signals obtained from a saxophone we demonstrate the potential of the method when applied to complex natural sounds.