New insights on the optimality of parameterized wiener filters for speech enhancement applications

TL;DR

This paper introduces a unified theoretical framework for noise reduction in speech enhancement, encompassing Wiener and binary masks, enabling optimal trade-offs and potential online adaptation for real-time applications.

Contribution

It provides a novel cost function that unifies various masks as optimal solutions, linking mask morphology with noise reduction and speech distortion trade-offs.

Findings

The conformable suppression mask achieves similar quality to classical Wiener filters.

The framework allows for online adaptation based on acoustic scenario variations.

Potential applications include hearing aids and cochlear implants.

Abstract

This work presents a unified framework for defining a family of noise reduction techniques for speech enhancement applications. The proposed approach provides a unique theoretical foundation for some widely-applied soft and hard time-frequency masks, which encompasses the well-known Wiener filter and the heuristically-designed Binary mask. These techniques can now be considered as optimal solutions of the same minimization problem. The proposed cost function is defined by two design parameters that not only establish a desired trade-off between noise reduction and speech distortion, but also provide an insightful relationship with the mask morphology. Such characteristic may be useful for applications that require online adaptation of the suppression function according to variations of the acoustic scenario. Simulation examples indicate that the derived conformable suppression mask has approximately the same quality and intelligibility performance capability of the classical heuristically-defined parametric Wiener filter. The proposed approach may be of special interest for real-time embedded speech enhancement applications such as hearing aids and cochlear implants.