Kenneth John Faller

Decomposition of Head Related Impulse Responses by Selection of Conjugate Pole Pairs

Currently, to obtain maximum fidelity 3D audio, an intended listener is required to undergo time consuming measurements using highly specialized and expensive equipment. Customizable Head-Related Impulse Responses (HRIRs) would remove this limitation. This paper reports our progress in the first stage of the development of customizable HRIRs. Our approach is to develop compact functional models that could be equivalent to empirically measured HRIRs but require a much smaller number of parameters, which could eventually be derived from the anatomical characteristics of a prospective listener. For this first step, HRIRs must be decomposed into multiple delayed and scaled damped sinusoids which, in turn, reveal the parameters (delay and magnitude) necessary to create an instance of the structural model equivalent to the HRIR under analysis. Previously this type of HRIR decomposition has been accomplished through an exhaustive search of the model parameters. A new method that approaches the decomposition simultaneously in the frequency (Z) and time domains is reported here.

Decomposition of Head-Related Transfer Functions into Multiple Damped and Delayed Sinusoidals

There are currently two options to achieve binaural sound spatialization using Head-Related Impulse Responses (HRIRs): measure every intended listener’s HRIR or use generic HRIRs. However, measuring HRIRs requires expensive and specialized equipment, which removes its availability to the general public. In contrast, use of generic HRIRs results in higher localization errors. Another possibility that researchers, including our group, are pursuing is the customization of HRIRs. Our group is pursuing this by developing a structural model in which the physical measurements of a new intended listener could be used to synthesize his/her custom-fitted HRIRs, to achieve spatialization equivalent to measured HRIRs. However, this approach requires that HRIRs from multiple subjects be initially broken down in order to reveal the parameters of the corresponding structural models. This paper presents a new method for decomposing HRIRs and tests its performance on simulated examples and actual HRIRs.

Decomposition of Head-Related Transfer Functions Based on the Hankel Total Least Squares Method

Head-Related Transfer Functions (HRTFs) are used to achieve binaural sound spatialization. Originally, sound spatialization techniques that utilize HRTFs require an intended listener to undergo lengthy measurements with specialized equipment. Unfortunately, the alternative generic HRTFs increase localization errors especially in elevation. Another option that we are pursuing is to customize HRTFs based on the physical measurements of a listener such that their performance is equivalent to measured HRTFs. However, an initial step of decomposing measured HRTFs in order to reveal the parameters of the required structural pinna model must be performed. A new approach for the decomposition of HRTFs is suggested and evaluated on simulated examples. Finally, the method is used to decompose actual HRTFs and the results are evaluated.

A new inverse processing approach to the modelling of head-related transfer functions for audio spatialization

Currently, achieving high-fidelity sound spatialization requires the prospective user to undergo lengthy measurements in an anechoic chamber using highly specialized equipment. This, in turn, has increased the cost and reduced the availability of high-fidelity spatialization to the general public. Attempts to generalize 3D audio have been made using the measurement of a KEMAR dummy head or creating a database containing a sample of the public. Unfortunately, this leads to increased front/back reversals and localization errors in the median plane. Customizable head-related impulse responses (HRIRs) would reduce the errors caused by general HRIRs and remove the limitation of the measured HRIRs. This article reports an initial stage in the development of customizable HRIRs. The ultimate goal is to develop a compact functional model that is equivalent to empirically measured HRIRs but requires a smaller number of parameters that could be obtained from the anatomical characteristics of the intended listener. In order to arrive at such a model, the HRIRs must be decomposed into multiple-scaled and delayed-damped sinusoids, which would reveal the parameters that the compact model needs to have an impulse response similar to the measured HRIR. Previously this type of HRIR decomposition has been accomplished through an exhaustive search of the model parameters. A new method that approaches the decomposition simultaneously in the frequency (Z) and time domains is reported here.

Modeling of Head-Related Transfer Functions Through Parallel Adaptive Filters

Currently, sound spatialization techniques that utilize “individual” Head-Related Transfer Functions (HRTFs) require the intended listener to undergo lengthy measurements with specialized equipment. Alternatively, the use of generic HRTFs may contribute to additional localization errors. A third possibility that we are pursuing is the customization of HRTFs, performed on the basis of geometrical measurements of the intended listener to determine the appropriate parameters in a structural HRTF model. However, an initial step of decomposing measured HRTFs in order to reveal the parameters of the structural model must be performed. A new approach for the decomposition of HRTFs is suggested and evaluated on simulated examples. The potential of this method for the decomposition of measured HRTFs is discussed.