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Efficient Approximation of Head-Related Transfer Functions in Subbands for Accurate Sound Localization.

ABSTRACT: Head-related transfer functions (HRTFs) describe the acoustic filtering of incoming sounds by the human morphology and are essential for listeners to localize sound sources in virtual auditory displays. Since rendering complex virtual scenes is computationally demanding, we propose four algorithms for efficiently representing HRTFs in subbands, i.e., as an analysis filterbank (FB) followed by a transfer matrix and a synthesis FB. All four algorithms use sparse approximation procedures to minimize the computational complexity while maintaining perceptually relevant HRTF properties. The first two algorithms separately optimize the complexity of the transfer matrix associated to each HRTF for fixed FBs. The other two algorithms jointly optimize the FBs and transfer matrices for complete HRTF sets by two variants. The first variant aims at minimizing the complexity of the transfer matrices, while the second one does it for the FBs. Numerical experiments investigate the latency-complexity trade-off and show that the proposed methods offer significant computational savings when compared with other available approaches. Psychoacoustic localization experiments were modeled and conducted to find a reasonable approximation tolerance so that no significant localization performance degradation was introduced by the subband representation.

SUBMITTER: Marelli D

PROVIDER: S-EPMC4678625 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Efficient Approximation of Head-Related Transfer Functions in Subbands for Accurate Sound Localization.

Marelli Damián D Baumgartner Robert R Majdak Piotr P

IEEE transactions on audio, speech, and language processing 20150701 7

Head-related transfer functions (HRTFs) describe the acoustic filtering of incoming sounds by the human morphology and are essential for listeners to localize sound sources in virtual auditory displays. Since rendering complex virtual scenes is computationally demanding, we propose four algorithms for efficiently representing HRTFs in subbands, i.e., as an analysis filterbank (FB) followed by a transfer matrix and a synthesis FB. All four algorithms use sparse approximation procedures to minimiz ...[more]

PMID: 26681930

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Project description:Thermally activated delayed florescence (TADF) is a mechanism that increases the electroluminescence efficiency in organic light-emitting diodes by harnessing both singlet and triplet excitons. TADF is facilitated by a small energy difference between the first singlet (S1) and triplet (T1) excited states (? E(ST)), which is minimized by spatial separation of the donor and acceptor moieties. The resultant charge-transfer (CT) excited states are difficult to model using time-dependent density functional theory (TDDFT) because of the delocalization error present in standard density functional approximations to the exchange-correlation energy. In this work we explore the application of the particle-particle random phase approximation (pp-RPA) for the determination of both S1 and T1 excitation energies. We demonstrate that the accuracy of the pp-RPA is functional dependent and that, when combined with the hybrid functional B3LYP, the pp-RPA computed ? E(ST) have a mean absolute deviation (MAD) of 0.12 eV for the set of examined molecules. A key advantage of the pp-RPA approach is that the S1 and T1 states are characterized as CT states for all of experimentally reported TADF molecules examined here, which allows for an estimate of the singlet-triplet CT excited state energy gap (? E(ST) = 1CT - 3CT). For experimentally known TADF molecules with a small (<0.2 eV) ? E(ST) in this data set, a high accuracy is demonstrated for the prediction of both the S1 (MAD = 0.18 eV) and T1 (MAD = 0.20 eV) excitation energies as well as ? E(ST) (MAD = 0.05 eV). This result is attributed to the consideration of correct antisymmetry in the particle-particle interaction leading to the use of full exchange kernel in addition to the Coulomb contribution, as well as a consistent treatment of both singlet and triplet excited states. The computational efficiency of this approach is similar to that of TDDFT, and the cost can be reduced significantly by using the active-space approach.

Dataset Information

Efficient Approximation of Head-Related Transfer Functions in Subbands for Accurate Sound Localization.

Publications

Efficient Approximation of Head-Related Transfer Functions in Subbands for Accurate Sound Localization.

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