Blockwise Subspace Identification for Active Noise Control

In this paper, a subspace identification solution is provided for active noise control (ANC) problems. The solution is related to so-called block updating methods, where instead of updating the (feedforward) controller on a sample by sample base, it is updated each time based on a block of N samples. The use of the subspace identification based ANC methods enables non-iterative derivation and updating of MIMO compact state space models for the controller. The robustness property of subspace identification methods forms the basis of an accurate model updating mechanism, using small size data batches. The design of a feedforward controller via the proposed approach is illustrated for an acoustic duct benchmark problem, supplied by TNO Institute of Applied Physics (TNO-TPD), the Netherlands. We also show how to cope with intrinsic feedback. A comparison study with various ANC schemes, such as block filtered-U, demonstrates the increased robustness of a subspace derived controlle

Sparse complex FxLMS for active noise cancellation over spatial regions

In this paper, we investigate active noise control over large 2D spatial regions when the noise source is sparsely distributed. The l1 relaxation technique originated from compressive sensing is adopted and based on that we develop the algorithm for two cases: multipoint noise cancellation and wave domain noise cancellation. This results in two new variants (i) zero-attracting multi-point complex FxLMS and (ii) zero-attracting wave domain complex FxLMS. Both approaches use a feedback control system, where a microphone array is distributed over the boundary of the control region to measure the residual noise signals and a loudspeaker array is placed outside the microphone array to generate the anti-noise signals. Simulation results demonstrate the performance and advantages of the proposed methods in terms of convergence rate and spatial noise reduction levels.This work is supported by Australian Research Council (ARC) Discovery Projects funding scheme (project no. DP140103412). The work of J. Zhang was sponsored by the China Scholarship Council with the Australian National University

Review of active noise control techniques with emphasis on sound quality enhancement

The traditional active noise control design aims to attenuate the energy of residual noise, which is indiscriminative in the frequency domain. However, it is necessary to retain residual noise with a specified spectrum to satisfy the requirements of human perception in some applications. In this paper, the evolution of active noise control and sound quality are briefly discussed. This paper emphasizes on the advancement of active noise control method in the past decades in terms of enhancing the sound quality

Estimation-based synthesis of H∞-optimal adaptive FIR filtersfor filtered-LMS problems

This paper presents a systematic synthesis procedure for H∞-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H∞ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal

Noise cancellation over spatial regions using adaptive wave domain processing

This paper proposes wave-domain adaptive processing for noise cancellation within a large spatial region. We use fundamental solutions of the Helmholtz wave-equation as basis functions to express the noise field over a spatial region and show the wave-domain processing directly on the decomposition coefficients to control the entire region. A feedback control system is implemented, where only a single microphone array is placed at the boundary of the control region to measure the residual signals, and a loudspeaker array is used to generate the anti-noise signals. We develop the adaptive wave-domain filtered-x least mean square algorithm. Simulation results show that using the proposed method the noise over the entire control region can be significantly reduced with fast convergence in both free-field and reverberant environmentsThanks to Australian Research Councils Discovery Projects funding scheme (project no. DP140103412). The work of J. Zhang was sponsored by the China Scholarship Council with the Australian National University

PPG Heart Rate Detection in the Presence of Motion Artifacts

Peripheral circulation can elicit a lot of relevant diagnostic information like heart rate and blood oxygenation level without the need of any invasive measurements. Photoplethysmographic (PPG) signals are obtained by such non-invasive measurements using pulse oximeters. PPG signals, although non-invasive, come with some inherent problems. In a nonhospital environment, like when using a wearable type of sensor, a measured PPG signal predominantly suffers from motion artifacts. Ambient light conditions, temperature, and respiratory artifacts are a few other noise sources that affect the PPG signals when trying to measure heart rates. Most motion artifacts lie in the same frequency range as that of the required noise free signal. So, simple filtering is unlikely to work. This work explores adaptive filtering techniques that are commonly used for noise removal. The current work also proposes to use a popular active noise cancellation technique combined with adaptive filtering and artificial neural networks to minimize the motion artifacts. Furthermore, the work proposes a wrapper algorithm that covers the deficiency of the other techniques. Finally, this work employs a smart peak identification technique to measure reliable heart rates from the MA reduced signals

PPG Heart Rate Detection in the Presence of Motion Artifacts

Peripheral circulation can elicit a lot of relevant diagnostic information like heart rate and blood oxygenation level without the need of any invasive measurements. Photoplethysmographic (PPG) signals are obtained by such non-invasive measurements using pulse oximeters. PPG signals, although non-invasive, come with some inherent problems. In a nonhospital environment, like when using a wearable type of sensor, a measured PPG signal predominantly suffers from motion artifacts. Ambient light conditions, temperature, and respiratory artifacts are a few other noise sources that affect the PPG signals when trying to measure heart rates. Most motion artifacts lie in the same frequency range as that of the required noise free signal. So, simple filtering is unlikely to work. This work explores adaptive filtering techniques that are commonly used for noise removal. The current work also proposes to use a popular active noise cancellation technique combined with adaptive filtering and artificial neural networks to minimize the motion artifacts. Furthermore, the work proposes a wrapper algorithm that covers the deficiency of the other techniques. Finally, this work employs a smart peak identification technique to measure reliable heart rates from the MA reduced signals

Deep Generative Fixed-filter Active Noise Control

Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises. Selective fixed-filter active noise control (SFANC) can significantly reduce response time by selecting appropriate pre-trained control filters for different noises. Nonetheless, the limited number of pre-trained control filters may affect noise reduction performance, especially when the incoming noise differs much from the initial noises during pre-training. Therefore, a generative fixed-filter active noise control (GFANC) method is proposed in this paper to overcome the limitation. Based on deep learning and a perfect-reconstruction filter bank, the GFANC method only requires a few prior data (one pre-trained broadband control filter) to automatically generate suitable control filters for various noises. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.Comment: Accepted by ICASSP 2023. Code will be available after publicatio