Sinusoidal modeling of audio and speech using psychoacoustic-adaptive matching pursuits

We propose a segment-based matching pursuit algorithm where the psychoacoustical properties of the human auditory system are taken into account. Rather than scaling the dictionary elements according to auditory perception, we define a psychoacoustic-adaptive norm on the signal space which can be used for assigning the dictionary elements to the individual segments in a rate-distortion optimal manner. The new algorithm is asymptotically equal to signal-to-mask ratio based algorithms in the limit of infinite analysis window length. However, the new algorithm provides a significantly improved selection of the dictionary elements for finite window length.