Abstract

The objective of binaural speech enhancement algorithms is to reduce the undesired noise component, while preserving the desired speech source and the binaural cues of all sound sources. For the scenario of a single desired speech source in a diffuse noise field, an extension of the binaural multichannel Wiener filter (MWF), namely the MWF-IC, has been recently proposed, which aims to preserve the interaural coherence (IC) of the noise component. However, due to the large complexity of the MWF-IC, in this paper we propose several alternative algorithms at a lower computational complexity. First, we consider a quasi-distortionless version of the MWF-IC, denoted as minimum-variance-distortionless response (MVDR-IC). Second, we propose to preserve the IC of the noise component using the binaural MWF with partial noise estimation (MWF-N) and the binaural MVDR beamformer with partial noise estimation (MVDR-N), for which closed-form expressions exist. In addition, we show that for the MVDR-N a closed-form expression can be derived for the tradeoff parameter yielding a desired magnitude squared coherence (MSC) for the output noise component. Since contrary to the MWF-IC and the MWF-N the MVDR-IC and the MVDR-N do not take into account the spectro-temporal properties of the speech and the noise components, we propose to apply a spectral postfilter to the filter outputs, improving the noise reduction performance. The performance of all algorithms is compared in several diffuse noise scenarios. The simulation results show that both the MVDR-IC and the MVDR-N are able to preserve the MSC of the noise component, while generally the MVDR-IC shows a slightly better noise reduction performance at a larger complexity. Further, simulation results show that applying a spectral postfilter leads to a very similar performance for all considered algorithms in terms of noise reduction and speech distortion.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.