2022•The Journal of the Acoustical Society of AmericaOpen access

Enhancing binaural rendering of head-worn microphone arrays through the use of adaptive spatial covariance matching

Janani Fernandez, Leo McCormack, Petteri Hyvärinen, Archontis Politis, Ville Pulkki

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Abstract

In this article, the application of spatial covariance matching is investigated for the task of producing spatially enhanced binaural signals using head-worn microphone arrays. A two-step processing paradigm is followed, whereby an initial estimate of the binaural signals is first produced using one of three suggested binaural rendering approaches. The proposed spatial covariance matching enhancement is then applied to these estimated binaural signals with the intention of producing refined binaural signals that more closely exhibit the correct spatial cues as dictated by the employed sound-field model and associated spatial parameters. It is demonstrated, through objective and subjective evaluations, that the proposed enhancements in the majority of cases produce binaural signals that more closely resemble the spatial characteristics of simulated reference signals when the enhancement is applied to and compared against the three suggested starting binaural rendering approaches. Furthermore, it is shown that the enhancement produces spatially similar output binaural signals when using these three different approaches, thus indicating that the enhancement is general in nature and could, therefore, be employed to enhance the outputs of other similar binaural rendering algorithms.

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In this article, the application of spatial covariance matching is investigated for the task of producing spatially enhanced binaural signals using head-worn microphone arrays. A two-step processing paradigm is followed, whereby an initial estimate of the binaural signals is first produced using one of three suggested binaural rendering approaches. The proposed spatial covariance matching enhancement is then applied to these estimated binaural signals with the intention of producing refined binaural signals that more closely exhibit the correct spatial cues as dictated by the employed sound-field model and associated spatial parameters. It is demonstrated, through objective and subjective evaluations, that the proposed enhancements in the majority of cases produce binaural signals that more closely resemble the spatial characteristics of simulated reference signals when the enhancement is applied to and compared against the three suggested starting binaural rendering approaches. Furthermore, it is shown that the enhancement produces spatially similar output binaural signals when using these three different approaches, thus indicating that the enhancement is general in nature and could, therefore, be employed to enhance the outputs of other similar binaural rendering algorithms.

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Available abstract

In this article, the application of spatial covariance matching is investigated for the task of producing spatially enhanced binaural signals using head-worn microphone arrays. A two-step processing paradigm is followed, whereby an initial estimate of the binaural signals is first produced using one of three suggested binaural rendering approaches. The proposed spatial covariance matching enhancement is then applied to these estimated binaural signals with the intention of producing refined binaural signals that more closely exhibit the correct spatial cues as dictated by the employed sound-field model and associated spatial parameters. It is demonstrated, through objective and subjective evaluations, that the proposed enhancements in the majority of cases produce binaural signals that more closely resemble the spatial characteristics of simulated reference signals when the enhancement is applied to and compared against the three suggested starting binaural rendering approaches. Furthermore, it is shown that the enhancement produces spatially similar output binaural signals when using these three different approaches, thus indicating that the enhancement is general in nature and could, therefore, be employed to enhance the outputs of other similar binaural rendering algorithms.

Key concepts: Binaural recording, Rendering (computer graphics), Computer science, Covariance, Microphone, Speech recognition, Matching (statistics), Covariance function

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