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

09/19/2018 ∙ by Rafael Attili Chiea, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 12

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.