Head-related transfer function interpolation in azimuth, elevation, and distance

Gamper, Hannes

doi:10.1121/1.4828983

Cited by 59 publications

(21 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our result from using tetrahedral interpolation with minimum phase HRIRs as input data, and SD HRTF minimum phase of 2.7959 dB, is comparable with that of tetrahedral interpolation with magnitude HRTFs as input data (Gamper, 2013a) and SD HRTF interpolation of 2.7852 dB. The framework proposed here is to interpolate measured HRTFs in 3D, namely azimuth, elevation, and distance, using tetrahedral interpolation with minimum phase HRIRs as its input and barycentric weights.…”

Section: Resultssupporting

confidence: 53%

See 1 more Smart Citation

Implementation of 3D HRTF Interpolation in Synthesizing Virtual 3D Moving Sound

Hugeng¹,

Anggara²,

Gunawan³

2017

IJTech

View full text Add to dashboard Cite

ABSTRACT3D sound is a new trend in various media, such as movies, video games, and musicals. Interpolated head-related transfer functions (HRTFs) are a key factor in its production, due to real-time system limitations in storing measured HRTFs. In addition, the interpolation of HRTFs can reduce the need to measure a large amount of HRTFs and the associated effort. In this research, we used the PKU-IOA HRTF Database and covered three interpolation techniques, namely bilinear rectangular, bilinear triangular, and tetrahedral. Bilinear interpolations can be used to compute weights in interpolating measured HRTFs in a linear fashion, with respect to azimuth and elevation angles. Such interpolations have been proposed for three measurement points that form a triangle or for four measurement points that form a rectangle, surrounding the HRTF at a desired point. These geometrical approaches compute weights from a distance of the desired point from each measurement point. Tetrahedral interpolation, meanwhile, is a technique for HRTF measurements in 3D (i.e. azimuth, elevation, and distance) using barycentric weights. Based on our experiments, 3D tetrahedral interpolation results in the best average mean square error (MSE) of 3.72% for minimum phase head related impulse responses (HRIRs) and best average spectral distortion (SD) of 2.79 dB for magnitude HRTFs, compared to 2D bilinear interpolations (i.e. rectangular and triangular interpolation). Regarding the latter, bilinear rectangular interpolation generally performs better than the triangular variety. Additionally, the use of minimum phase HRIRs as input data results in more optimal interpolated data than magnitude HRTFs. We therefore propose an optimal framework for obtaining estimated HRIRs by interpolating minimum phase HRIRs using tetrahedral interpolation. Such HRIRs have been simulated to produce virtual 3D moving sound in a horizontal plane with a difference of 2.5 o of azimuth angle. The simulated moving sound that is heard moves naturally in a clockwise direction from an azimuth angle of 0 o to 360 o .

show abstract

Section: Resultssupporting

confidence: 53%

“…Figure 4 Graphical interpretation of tetrahedral interpolation (Gamper, 2013a) The weights g i are the barycentric coordinates of point X. To estimate the target HRTF x Ĥ at point X as the weighted sum of the HRTFs, H i , measured at A, B, C, and D, we can use the barycentric coordinates as interpolation weights, as follows:…”

Section: Tetrahedral Interpolationmentioning

confidence: 99%

Implementation of 3D HRTF Interpolation in Synthesizing Virtual 3D Moving Sound

Hugeng¹,

Anggara²,

Gunawan³

2017

IJTech

View full text Add to dashboard Cite

show abstract

“…Parametric approaches allow to describe HRTFs with a limited set of parameters instead of the whole set of filter coefficients, reducing the amount of information needed to describe a complete HRTF dataset. Moreover, parametric methods result in additional benefits such as the simplification of the interpolation procedures needed for synthesizing HRTFs corresponding to spatial angles missing in the original HRTF dataset [10], [11]. While the previous work in [7] was shown to provide all the above benefits by implementing a chain of second-order sections (SOS), the data dependencies within the proposed sequential structure prevent taking advantage of current parallel processors embedded into state-of-the-art devices.…”

Section: Introductionmentioning

confidence: 96%

A Parallel Approach to HRTF Approximation and Interpolation Based on a Parametric Filter Model

Ramos

Cobos

Bank

et al. 2017

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

Abstract-Spatial audio rendering techniques using headrelated transfer functions (HRTFs) are currently used in many different contexts such as immersive teleconferencing systems, gaming, or 3D audio reproduction. Since all these applications usually involve real-time constraints, efficient processing structures for HRTF modeling and interpolation are necessary for providing real-time binaural audio solutions. This letter presents a parametric parallel model that allows to perform HRTF filtering and interpolation efficiently from an input HRTF dataset. The resulting model, which is an adaptation from a recently proposed modeling technique, not only reduces the size of HRTF datasets significantly, but it allows for simplified interpolation and real-time computation over parallel processors. In order to discuss the suitability of this new model, an implementation over a graphic processing unit (GPU) is presented.

show abstract

“…It permits to compare the signals received at the ears to a reference signal (the source signal) and provides information such as elevation and distance between the source and the ears [9].…”

Section: Introductionmentioning

confidence: 99%

Assessment of sound source localization of an intra-aural audio wearable device for audio augmented reality applications

Lezzoum

Voix

2016

2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)

View full text Add to dashboard Cite

Abstract-This paper presents a study on the effect of an intraaural Audio Wearable Device (AWD) on sound source localization. The AWD used in this study is equipped with a miniature outer-ear microphone, a miniature digital signal processor and a miniature internal loudspeaker in addition to other electronics. It is aimed for audio augmented reality applications, for example to play 3D audio sounds and stimuli while keeping the wearer protected from loud or unwanted ambient noise. This AWD is evaluated in terms of ambient source localization using three localization cues computed using signals played from different positions in the horizontal and sagittal planes and recorded in the ear canals of an artificial head with and without a pair of AWDs. The localization cues are: the interaural time difference, the interaural level difference, and the head related transfer functions. Results showed that the used AWD does barely affect the localization of low frequency sounds with localization error around 2 0 , and only affects the localization of higher frequency sound depending on their position and frequency range.

show abstract

Head-related transfer function interpolation in azimuth, elevation, and distance

Cited by 59 publications

References 14 publications

Implementation of 3D HRTF Interpolation in Synthesizing Virtual 3D Moving Sound

Implementation of 3D HRTF Interpolation in Synthesizing Virtual 3D Moving Sound

A Parallel Approach to HRTF Approximation and Interpolation Based on a Parametric Filter Model

Assessment of sound source localization of an intra-aural audio wearable device for audio augmented reality applications

Contact Info

Product

Resources

About