A Joint Perspective of Periodically Excited Efficient NLMS Algorithm and Inverse Cyclic Convolution

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

doi:10.1109/icassp.2018.8461341

Cited by 4 publications

(2 citation statements)

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“…Adaptive filtering method, such as the normalized least-mean-square (NLMS) algorithm or variants thereof are often applied to estimate the IRs. The NLMS algorithm can also be related to deconvolution used in static measurements [25]. Some other adaptive filtering methods can be interpreted as state estimation techniques [26] to obtain IRs as state estimates, as, for example, applied in the time domain for ANC in [27] or in the discrete Fourier transform (DFT) domain for AEC in [26].…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…Recently, exponential-sweep measurements with slow continuous subject rotations were investigated [18]. Moreover, it was shown that deconvolution and NLMS-based system identification are algorithmically equivalent for a single-loudspeaker setup [19].…”

Section: Introductionmentioning

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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A Joint Perspective of Periodically Excited Efficient NLMS Algorithm and Inverse Cyclic Convolution

Cited by 4 publications

References 10 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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