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Ali Khajeh-Saeed

dblp:90/9747 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0006-1046-1830ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › spatial audio
head-related transfer function
0.712023
Verification on Head-Related Transfer Functions of a Snowman Model Simulated Using the Finite-Difference Time-Domain Method · IEEE ACM Trans. Audio Speech Lang. Process. 2023
Audio and music processing
spatial audio
0.712023
Verification on Head-Related Transfer Functions of a Snowman Model Simulated Using the Finite-Difference Time-Domain Method · IEEE ACM Trans. Audio Speech Lang. Process. 2023
Audio and music processing › room acoustics › room acoustics simulation
finite-difference time-domain method
0.212023
Verification on Head-Related Transfer Functions of a Snowman Model Simulated Using the Finite-Difference Time-Domain Method · IEEE ACM Trans. Audio Speech Lang. Process. 2023
Audio and music processing
room acoustics
0.212023
Verification on Head-Related Transfer Functions of a Snowman Model Simulated Using the Finite-Difference Time-Domain Method · IEEE ACM Trans. Audio Speech Lang. Process. 2023

Methods — techniques the papers use, named apart from their topics

solution verification · 0.7multipole reexpansion · 0.7code verification · 0.7
YearPublicationVenuePosition
2023 Verification on Head-Related Transfer Functions of a Snowman Model Simulated Using the Finite-Difference Time-Domain Method
abstract
Although finite-difference time-domain (FDTD) simulations are extensively used in acoustics, studies assessing the reliability and the accuracy of the implemented method are scarce. Moreover, the operational validity of a simulation method is context-dependent. Motivated by the context of head-related transfer function (HRTF) prediction, this work presents a verification procedure for FDTD-simulated HRTFs from a simplified model of a human head and torso which comprises two spheres and is known as the snowman model. The analytic solution required by the code verification is computed with the multipole reexpansion technique and used to compute convergence rate estimates. A solution verification process follows in which asymptotic predictions are computed. Results from the code verification show scattered convergence rates which attained the expected first-order accuracy at frequencies below 1 kHz when a linear regression model was used as the estimation procedure. Results from the solution verification show that the asymptotic predictions are accurate up to 10 kHz, after which bias is observed. Assessing the accuracy of the employed solution verification procedure revealed that the absolute difference between the$\log$-magnitude of the asymptotic predictions and this of the analytic solution is within 1 dB up to 11589 Hz across the HRTF directions considered.
Julie Meyer, Sebastian Prepelita, Ali Khajeh-Saeed, Michael Smirnov, Pablo Hoffmann
IEEE ACM Trans. Audio Speech Lang. Process.3