Abstract
Binaural room auralization involves Binaural Room Impulse Responses (BRIRs). Dynamic binaural synthesis (i.e., head-tracked presentation) requires BRIRs for multiple head poses. Artificial heads can be used to measure BRIRs, but BRIR modeling from microphone array room impulse responses (RIRs) is becoming popular since personalized BRIRs can be obtained for any head pose with low extra effort. We present a novel framework for estimating a binaural signal from microphone array signals, using causal Wiener filtering and polynomial matrix formalism. The formulation places no explicit constraints on the geometry of the microphone array and enables directional weighting of the estimation error. A microphone noise model is used for regularization and to balance filter performance and noise gain. A complete procedure for BRIR modeling from microphone array RIRs is also presented, employing the proposed Wiener filtering framework. An application example illustrates the modeling procedure using a 19-channel spherical microphone array. Direct and reflected sound segments are modeled separately. The modeled BRIRs are compared to measured BRIRs and are shown to be waveform-accurate up to at least 1.5 kHz. At higher frequencies, correct statistical properties of diffuse sound field components are aimed for. A listening test indicates small perceptual differences to measured BRIRs. The presented method facilitates fast BRIR data set acquisition for use in dynamic binaural synthesis and is a viable alternative to Ambisonics-based binaural room auralization.
Highlights
A UDITORY experiences are defined by the sound that enters the ear canals
We motivate this choice partly by that we make use of directional error weighting in the application of the framework to Binaural Room Impulse Responses (BRIRs) modeling, and partly by that from an information-theoretic perspective, we argue that two linear & time-invariant (LTI) filters in series cannot produce a better estimate of the binaural signal than a single LTI filter
While the filter design framework puts no restrictions on the array geometry, a spherical microphone array is suitable for this application because the design of the binaural estimation filter Fr(q−1), for the reflected sound part of the BRIR, makes use of its approximately uniform beamforming performance in all directions
Summary
A UDITORY experiences are defined by the sound that enters the ear canals. By reproducing the ear signals corresponding to a real or simulated acoustic event using headphones or loudspeakers, the auditory sensation of the original event can be replicated [1]–[3]. It is more general with regard to the flexible sound field and microphone noise models used compared to the problem formulations for direct binaural estimation filter design presented in the references cited above. We use direct estimation ( the framework can be used to design filters for use with Ambisonics as well, see Section V) We motivate this choice partly by that we make use of directional error weighting in the application of the framework to BRIR modeling (which is not straightforward with Ambisonics), and partly by that from an information-theoretic perspective (and using our problem formulation), we argue that two linear & time-invariant (LTI) filters in series (for calculation of intermediate SH-signals) cannot produce a better estimate of the binaural signal than a single LTI filter (in a mean-square error sense). Compared to [25] we present a complete filter design process for BRIR modeling that includes DoA-estimation of the direct sound and inversion of the microphone array dynamics.
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