arXiv subjects

Jan Wouters

Publications and source records attributed to Jan Wouters.

3 recordsLinked to original sources

arXiv2026

HIDVAS: A Hearing Instrument Dataset in Various Acoustical Scenarios for Algorithm Evaluation and Training

To evaluate the performance of audio signal processing algorithms and to train data-driven algorithms, e.g., as applied in hearing instruments, either simulated or recorded data can be used. While large batches of simulated data can be generated using mathematical models, recorded data provide a more adequate representation of real-life scenarios. Therefore, in this paper, the Hearing Instrument Dataset in Various Acoustical Scenarios (HIDVAS) is introduced. This dataset consists of both impulse responses and audio recordings using eight external loudspeakers, two external microphones, and a dummy head. On this dummy head behind-the-ear (BTE) hearing instrument shells with two microphones per shell are mounted, and in the dummy head's ears receiver-in-canal (RIC) hearing instrument loudspeakers are inserted. The dummy head also contains microphones located at its eardrum. The impulse responses have been computed from a swept-sine recording for each microphone-loudspeaker pair, and the audio recordings have been obtained by playing back audio (male and female speech, speech shaped noise, singing voice, stringed instrument, wind instrument, and percussion instrument) through each individual loudspeaker and recording simultaneously using all microphones. These recordings have been repeated for four hearing instrument domes (open, semi-open, closed, and no-RIC) in three reverberation conditions in one room (T30 = 0.09 s, T30 = 0.47 s, and T30 = 0.73 s), and in one reverberation condition in a different room (T30 = 1.48 s). The usage of the dataset as a `hearing instrument in a box' is exemplified with three example use cases.

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arXiv2025

Identifiability Conditions for Acoustic Feedback Cancellation with the Two-Channel Adaptive Feedback Canceller Algorithm

In audio signal processing applications with a microphone and a loudspeaker within the same acoustic environment, the loudspeaker signals can feed back into the microphone, thereby creating a closed-loop system that potentially leads to system instability. To remove this acoustic coupling, prediction error method (PEM) feedback cancellation algorithms aim to identify the feedback path between the loudspeaker and the microphone by assuming that the input signal can be modelled by means of an autoregressive (AR) model. It has previously been shown that this PEM framework and resulting algorithms can identify the feedback path correctly in cases where the forward path from microphone to loudspeaker is sufficiently time-varying or non-linear, or when the forward path delay equals or exceeds the order of the AR model. In this paper, it is shown that this delay-based condition can be generalised for one particular PEM-based algorithm, the so-called two-channel adaptive feedback canceller (2ch-AFC), to an invertibility-based condition, for which it is shown that identifiability can be achieved when the order of the forward path feedforward filter exceeds the order of the AR model. Additionally, the condition number of inversion of the correlation matrix as used in the 2ch-AFC algorithm can serve as a measure for monitoring the identifiability.

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arXiv2024

Integrated Minimum Mean Squared Error Algorithms for Combined Acoustic Echo Cancellation and Noise Reduction

In many speech recording applications, noise and acoustic echo corrupt the desired speech. Consequently, combined noise reduction (NR) and acoustic echo cancellation (AEC) is required. Generally, a cascade approach is followed, i.e., the AEC and NR are designed in isolation by selecting a separate signal model, separate cost function, and separate solution strategy. The AEC and NR are then cascaded one after the other, not accounting for their interaction. In this paper, an integrated approach is proposed to consider this interaction in a general multi-microphone/multi-loudspeaker setup. Therefore, a single signal model of either the microphone signal vector or the extended signal vector, obtained by stacking microphone and loudspeaker signals, is selected, a single mean squared error cost function is formulated, and a common solution strategy is used. Using this microphone signal model, a multi-channel Wiener filter (MWF) is derived. Using the extended signal model, it is shown that an extended MWF (MWFext) can be derived, and several equivalent expressions can be found, which are nevertheless shown to be interpretable as cascade algorithms. Specifically, the MWFext is shown to be equivalent to algorithms where the AEC precedes the NR (AEC-NR), the NR precedes the AEC (NR-AEC), and the extended NR (NRext) precedes the AEC and post-filter (PF) (NRext-AEC-PF). Under rank-deficiency conditions the MWFext is non-unique. Equivalence then amounts to the expressions being specific, not necessarily minimum-norm solutions, for this MWFext. The practical performances differ due to non-stationarities and imperfect correlation matrix estimation, with the AEC-NR and NRext-AEC-PF attaining best overall performance.

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