Tracking of Time-Variant Linear Systems: Influence of Group Delay for Different Excitation Signals

Kühl, Stefan; Nagel, Sebastian; Kabzinski, Tobias; Antweiler, Christiane; Jax, Peter

doi:10.1109/iwaenc.2018.8521372

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The curve for step size 0.5 appears to be slightly delayed as a result of the implicit temporal smoothing of the NLMS algorithm for step sizes less than one. Both of these observations match the analyses in [45]. Overall, this result demonstrates that a significant improvement can be achieved with the proposed framework despite the simplifying assumptions about the matrix-valued parameters.…”

Section: A Hrtf Measurement With 37 Channelssupporting

confidence: 80%

“…The minima occur temporally close to but slightly before the expected position, which is suspected to be a consequence of the head-tracking system's latency and/or mechanical imperfections. The delay in attaining the minimum SD n with the NLMS algorithm is a consequence of the systematic delay of half a period length, i.e., 4800 samples (0.1 s), as also analyzed in [45].…”

Section: B Two-loudspeaker Hrtf Measurementmentioning

confidence: 99%

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A Flexible Framework for Expectation Maximization-Based MIMO System Identification for Time-Variant Linear Acoustic Systems

Kabzinski,

Jax

2023

IEEE Open J. Signal Process.

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Quasi-continuous system identification of time-variant linear acoustic systems can be applied in various audio signal processing applications when numerous acoustic transfer functions must be measured. A prominent application is measuring head-related transfer functions. We treat the underlying multiple-input-multiple-output (MIMO) system identification problem in a state-space model as a joint estimation problem for states, representing impulse responses, and state-space model parameters using the expectation maximization (EM) algorithm. We address limitations of prior work by imposing different model structures, especially for dependencies within a (transformed) state vector. This results in block diagonal matrix structures, for which we derive M-step update rules. Making assumptions about this model structure and choosing a block size for a given application define the computational complexity. In examples, we found that applying this framework yields improvements of up to 10 dB in relative system distance in comparison to a conventional method.

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Section: A Hrtf Measurement With 37 Channelssupporting

confidence: 80%

Section: B Two-loudspeaker Hrtf Measurementmentioning

confidence: 99%

A Flexible Framework for Expectation Maximization-Based MIMO System Identification for Time-Variant Linear Acoustic Systems

Kabzinski,

Jax

2023

IEEE Open J. Signal Process.

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“…As expected from [37], the system identification using PSEQ excitation yields lower system distances than using white-noise excitation. In [38] it was analyzed that the estimated coefficients, compared to the true coefficients, appear to be systematically shifted by half the period length. To take this systematic shift of LS/2 samples into account, we compute a modified system distance ( ) comparing h k−LS/2,s to ĥk,s .…”

Section: Experiments and Resultsmentioning

confidence: 99%

Towards Faster Continuous Multi-Channel HRTF Measurements Based on Learning System Models

Kabzinski,

Jax

2021

Preprint

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Measuring personal head-related transfer functions (HRTFs) is essential in binaural audio. Personal HRTFs are not only required for binaural rendering and for loudspeaker-based binaural reproduction using crosstalk cancellation, but they also serve as a basis for data-driven HRTF individualization techniques and psychoacoustic experiments. Although many attempts have been made to expedite HRTF measurements, the rotational velocities in today's measurement systems remain lower than those in natural head movements. To cope with faster rotations, we present a novel continuous HRTF measurement method. This method estimates the HRTFs offline using a Kalman smoother and learns state-space parameters, including the system model, on short signal segments, utilizing the expectation maximization algorithm. We evaluated our method in simulated single-channel and multi-channel measurements using a rigid sphere HRTF model. Comparing with conventional methods, we found that the system distances are improved by up to 30 dB.

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Towards Faster Continuous Multi-Channel HRTF Measurements Based On Learning System Models

Kabzinski

Jax

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Tracking of Time-Variant Linear Systems: Influence of Group Delay for Different Excitation Signals

Cited by 5 publications

References 8 publications

A Flexible Framework for Expectation Maximization-Based MIMO System Identification for Time-Variant Linear Acoustic Systems

A Flexible Framework for Expectation Maximization-Based MIMO System Identification for Time-Variant Linear Acoustic Systems

Towards Faster Continuous Multi-Channel HRTF Measurements Based on Learning System Models

Towards Faster Continuous Multi-Channel HRTF Measurements Based On Learning System Models

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