Furthermore, in practice, such processing is not always guaranteed to produce output signals that have the intended interchannel relationships dictated by the employed sound-field model. However, due to the nature of time-frequency processing, the signal fidelity of the output signals may be degraded. These methods are often built on perceptually motivated sound-field models and estimate the spatial parameters over time and frequency, subsequently using this information to map the input signals to the binaural channels in an adaptive and more informed manner. Signal-dependent binaural rendering alternatives, on the other hand, have been demonstrated to surpass linear rendering methods in terms of the perceived spatial accuracy 15–18 when using the same number or fewer input channels. However, the spatial accuracy of the reproduction is inherently limited by the number of microphones in the array. 2,14 As a result of the linear mapping of signals, these methods retain high signal fidelity. 10–13 Other linear methods include binaural beamforming approaches. SMAs also allow for convenient conversions of the microphone array signals into spherical harmonic signals with numerous signal-independent proposals available for mapping these signals to the binaural channels. Traditionally, spherical microphone arrays (SMAs) with uniform sensor distributions have been popular for spatial audio capturing and reproduction due to their consistent spatial resolution for all directions. Furthermore, although such wearable devices have historically been limited in terms of hardware, it may be argued that with the introduction of a data-link in binaural hearing aids and as more sensors are integrated into future models, such devices are converging toward the high-sensor count microphone arrays used for high resolution spatial audio applications. However, it should be emphasized that one important design criteria, which is relevant to all modern head-worn devices and considered in recent related research, 4–9 is the preservation of sound source localization cues. 5–7 While there are similarities between the binaural rendering algorithms intended for AR/VR devices and those intended for binaural hearing aids, it should be acknowledged that there are some differing requirements. 1–5 In the context of hearing assistive devices, such as hearing aids, the relatively recent trend of including a data-link between devices has also prompted new proposals that take advantage of this freedom to share signals. The binaural reproduction of sound scenes captured using wearable microphone arrays has gained renewed interest in recent years with such arrays now being integrated into head-worn devices and used for augmented and virtual reality (AR/VR) applications. 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. 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. 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. 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. 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.
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