Feedback active noise control (ANC) systems are effective in reducing predictable noise, e.g. periodic, narrowband and colored noise. There are still few studies on the theoretical analysis of feedback ANC systems, and are limited to idealized signals such as sinusoidal or Gaussian signals. This paper presents the stochastic analysis of a feedback ANC system based on the filtered-x least mean square (FxLMS) algorithm, which is not relying on a specific noise model and perfect secondary path. The equations for the mean and mean-square convergence behavior are derived. Extensive simulations of sinusoidal, band-limited white noise, and hybrid signals illustrate the accuracy of the analysis.

Most of the traditional active noise control (ANC) systems are implemented using digital signal processor (DSP), but the lack of computational power of DSP has been a limitation to the application and development of ANC technology. Currently, it is a feasible approach to utilize Graphics Processing Unit (GPU) to enhance the computational power of the ANC system. The advantage of GPU is parallel computation, but most of the traditional ANC algorithms can not be efficiently executed in parallel. In this letter, a parallelizable Fast Multi-channel Wiener Filter (FMWF) algorithm is proposed, and the feasibility of implementing the FMWF algorithm on GPU is verified through experiments, which show that the FMWF algorithm has obvious advantages in parallel execution on GPU. In addition, a DSP-CPU-GPU architecture for ANC systems is designed. In this architecture, each processor can make full use of its own advantages to enhance the computational capability of the system and guarantee the real-time processing of the signals at the same time.

Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular acoustic settings, often show restricted generalization capabilities for diverse acoustic environments. Furthermore, obtaining annotated samples for spatial sound events is notably costly. Deploying a SELD system in a new environment requires extensive time for re-training and fine-tuning. To overcome these challenges, we propose environment-adaptive Meta-SELD, designed for efficient adaptation to new environments using minimal data. Our method specifically utilizes computationally synthesized spatial data and employs Model-Agnostic Meta-Learning (MAML) on a pre-trained, environment-independent model. The method then utilizes fast adaptation to unseen real-world environments using limited samples from the respective environments. Inspired by the Learning-to-Forget approach, we introduce the concept of selective memory as a strategy for resolving conflicts across environments. This approach involves selectively memorizing target-environment-relevant information and adapting to the new environments through the selective attenuation of model parameters. In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments. We evaluate our proposed method on the development set of the Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23) dataset and computationally synthesized scenes. Experimental results demonstrate the superior performance of the proposed method compared to conventional supervised learning methods, particularly in localization.

Polyphonic sound event localization and detection (SELD) aims at detecting types of sound events with corresponding temporal activities and spatial locations. In this paper, a trackwise ensemble event independent network with a novel data augmentation method is proposed. The proposed model is based on our previous proposed Event-Independent Network V2 and is extended by conformer blocks and dense blocks. The track-wise ensemble model with track-wise output format is proposed to solve an ensemble model problem for track-wise output format that track permutation may occur among different models. The data augmentation approach contains several data augmentation chains, which are composed of random combinations of several data augmentation operations. The method also utilizes log-mel spectrograms, intensity vectors, and Spatial Cues-Augmented Log-Spectrogram (SALSA) for different models. We evaluate our proposed method in the Task of the L3DAS22 challenge and obtain the top ranking solution with a location-dependent F-score to be 0.699. Source code is released1.

Distributed narrowband active noise control (NANC) systems using diffusion filtered-x least mean square (FxLMS) algorithm can effectively suppress the low-frequency periodic noise generated by rotating machinery. The computational burden is dispersed among the nodes over the acoustic sensor networks. The frequency of the reference signal is usually identified by a non-acoustic sensor in a NANC system. However, the frequency of the reference signal will be different from the primary noise frequency due to inevitable aging and fatigue accumulation and the control performance of NANC degrades considerably. This phenomenon is referred to as frequency mismatch (FM). In this letter, we analyze the performance degradation of the diffusion FxLMS algorithm due to FM in an NANC system. A theoretical model of the diffusion FxLMS algorithm in the presence of FM is derived based on the equivalent transfer function approach. Extensive simulations are performed to confirm the validity of the theoretical analysis.

The narrowband active noise control (ANC) algorithm works well in the multi-tone noise reduction system. However, the performance of this algorithm degenerates significantly when the noisy frequencies become very close (e. g. frequency gap is 3 Hz where the sampling frequency is 5000 Hz), which can be seen as frequency interference effects. Up to now, however, this effect has not been researched in detail. In this work, the frequency interference effects are analyzed and discussed. The convergence process, the noise reduction value, the stability condition and the optimum step size are obtained by extensive math formulation derivation, provided two frequencies are close and fixed. Theoretical results show that, tones with large frequency difference can be approximated to independent convergence while the system with close frequencies suffers from poor convergence rate. The value of step size plays an important role in convergence, but the interference effects could never be eliminated by adjusting step size. Simulations are conducted to verify the analysis and the results match well with the analysis.

This paper studies the diffusion (Diff) control for multichannel ANC systems, where a group of controllers and error microphones are physically distributed at different locations within a large area. In this case, the conventional consensus agreement for controllers cannot be reached. To solve this problem, a new Diff filtered-x least mean squares (Diff-FxLMS) algorithm that incorporates the knowledge of spatial smoothness is proposed. Compared to conventional Diff control criteria that have a fixed spatial smoothing strategy, the proposed Diff-FxLMS adjusts a so called spatial regularization (SR) coefficient adaptively such that the neighboring controllers keep their decision variables close to one another to minimize the global cost function while giving preference to possibly distinct local signals. A detailed performance analysis is carried out and verified by simulation. Based on the analysis, a variable SR formula is derived. The performance of the proposed algorithm is also compared with conventional methods.

This paper develops a framework for the design of diffusion adaptive algorithms, where a network of nodes aim to estimate system parameters from the collected distinct local data stream. We explore the time and spatial knowledge of system responses and model their evolution in both time and spatial domain. A weighted maximum a posteriori probability (MAP) is used to derive an adaptive estimator, where recent data has more influence on statistics via weighting factors. The resulting recursive least squares (RLS) local estimate can be implemented by the QR decomposition (QRD). To mediate the distinct spatial information incorporation within neighboring estimates, a variable spatial regularization (VSR) parameter is introduced. The estimation bias and variance of the proposed algorithm are analyzed. A new diffusion VSR QRRLS (Diff-VSR-QRRLS) algorithm is derived that balances the bias and variance terms. Simulations are carried out to illustrate the effectiveness of the theoretical analysis and evaluate the performance of the proposed algorithm.

Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most “natural” sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching, and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of “occlusion,” improved robustness to regularization, and improved broadband equalization.

The authors previously proposed an improved multiband‐structured subband adaptive filter (IMSAF) algorithm. In this contribution, they first present two delayless structures of the IMSAF algorithm to remove delay in the signal path. Then, they study the transient and steady‐state behaviour of the IMSAF algorithms based on the energy conservation arguments and paraunitary condition imposed on the analysis and synthesis filter banks. The analysis does not require a model for the input signal. Simulation results show that the proposed delayless IMSAF algorithm has a faster convergence rate than the traditional delayless subband adaptive filtering algorithms. Theoretical analysis of the IMSAF algorithm is in good agreement with computer simulation results.

In many practical situations, where the length of the system impulse response is extremely long, the adaptive filter usually works in an under-modeling situation. In our previous work, we found that for deficient-length frequency-domain block least-mean-square (FBLMS) algorithm, the steady-state solution depends on the step size selected. The deficient length-FBLMS algorithm will converge to the Wiener solution only if the same step size is selected for each frequency bin. Numerous variable step-size methods for FBLMS algorithm have been proposed. However, almost all of them cannot converge to the Wiener solution in the under-modeling situation. In this letter, a step size control method for deficient-length FBLMS algorithm is proposed. Effectiveness of the proposed algorithm is demonstrated through computer simulations.

Recently, the general Volterra filter (VF) has been adopted for the modeling of parametric loudspeakers. However, the computation complexity of the VF is too high for real-time implementation. In this paper, a one-dimension Volterra filter (ODVF) with much lower complexity is introduced to model and compensate for the nonlinearity of parametric loudspeakers. A theoretical framework for ODVF model identification is established and a method of measuring the ODVF kernels using the exponential swept-sine signal is provided. The validity of modeling the nonlinearity of the parametric loudspeaker using the ODVF is verified theoretically and experimentally. Based on the established ODVF model, an inverse filter is designed to compensate for the 2nd harmonic distortion of the parametric loudspeakers. To further reduce the 3rd harmonic distortion, an improved compensation method is also proposed. Experimental results show that the performance of the ODVF-based compensation is comparable to that of the Volterra-filter based compensation.

The acoustic contrast control (ACC) approach provides a simple strategy to focus sound on a designed target area in a personal audio system. All of the traditional ACC approaches have been designed in the frequency domain, and then transformed into the time domain through the application of an inverse Fourier transform. Therefore, the broadband contrast performance of the traditional ACC approach is not optimum. Especially, when the length of the control filter is short, the traditional approach may have poor contrast performance at non-control frequencies. This study proposes a novel method that can achieve the optimum broadband contrast performance, which also maintains a good frequency-response consistency by introducing the response variation constraints to improve sound quality. Experimental results demonstrate the effectiveness of the new method.

Recently, a multiband-structured subband adaptive filter (MSAF) algorithm was proposed to speed up the convergence of the normalized least-mean-square (NLMS) algorithm. In this letter, we extend this work and propose an improved multiband-structured subband adaptive filter (IMSAF) algorithm to increase the convergence speed of the MSAF, which can also be regarded as a unifying framework for the NLMS, MSAF, and affine projection (AP) algorithms. The proposed optimization criterion is based on the principle of minimal disturbance, canceling the most recentPa posteriori errors in each of theNsubbands. The stability condition and the computational complexity are also analyzed. Computer simulations in the context of system identification demonstrate the effectiveness of the new algorithm.

An open-loop stereophonic acoustic echo suppression (SAES) method without preprocessing is presented for teleconferencing systems, where the Wiener filter in the short-time Fourier transform (STFT) domain is employed. Instead of identifying the echo path impulse responses with adaptive filters, the proposed algorithm estimates the echo spectra from the stereo signals using two weighting functions. The spectral modification technique originally proposed for noise reduction is adopted to remove the echo from the microphone signal. Moreover, a priori signal-to-echo ratio (SER) based Wiener filter is used as the gain function to achieve a trade-off between musical noise reduction and computational load for real-time operations. Computer simulation shows the effectiveness and the robustness of the proposed method in several different scenarios.

This paper proposes a windowing frequency domain adaptive filter and an upsampling block transform preprocessing to solve the stereo acoustic echo cancellation problem. The proposed adaptive filter uses windowing functions with smooth cutoff property to reduce the spectral leakage during filter updating, so the utilization of the independent noise introduced by preprocessing in stereo acoustic echo cancellation can be increased. The proposed preprocessing is operated in short blocks with low processing delay, and it uses frequency-domain upsampling to meet the minimal block length requirement given by the band limit of simultaneous masking. Therefore, the simultaneous masking can be well utilized to improve the audio quality. The acoustic echo cancellation simulations and the audio quality evaluation show that, the proposed windowing frequency domain adaptive filter performs better than the conventional frequency domain adaptive filter in both mono and stereo cases, and the upsampling block transform preprocessing provides better audio quality and stereo acoustic echo cancellation performance than the half-wave preprocessing at the same noise level.

When implementing active noise control (ANC) systems, the filtered-x LMS (FXLMS) algorithm is often used, where the secondary path transfer function between the output of the adaptive control filter and the error sensor has to be modeled. To model the secondary path precisely, the secondary path estimation filter should be of sufficient order. However, too large an order increases the computation load of the FXLMS algorithm. In this letter, the relationship between the modeling phase error and the order of the secondary path modeling filter is analyzed.

Common active noise control (ANC) algorithms need to identify the secondary path transfer functions between the output of the adaptive control filters and the error sensors, and then use the information to guide the direction of control filter coefficient updating. Recently, Zhou et al. proposed an ANC algorithm without secondary path identification, and we improve their algorithm in this paper. For single-tone and narrowband noise control, the direction of control filter coefficient updating has four choices 180deg, 0deg, and plusmn90deg. We test the four update directions and select the one that works the best. If for all four update directions, the system converges slowly or diverges, we adjust the step size and test again with the new step size. The multitone and broadband noise control problems are converted into several single-tone and narrowband noise control problems by means of a frequency-domain delayless subband architecture. Compared to Zhou's algorithm, our proposed method yields good performance and converges quickly. Simulation results confirm the effectiveness of our proposed algorithm.

An efficient active noise control algorithm based on the delay less subband adaptive filter was previously proposed by Park (ldquoA delayless subband active noise control system for wideband noise control,IEEEtransactionsonspeechandaudioprocessing, vol. 9, no. 8, pp. 892-897, November 2001), whereby the computational complexity is reduced significantly by using a short impulse response filter to model the secondary path transfer function in a subband-decomposed form. In this correspondence, an overlap-save frequency-domain implementation of the algorithm is proposed. Simulations show that the proposed implementation has better performance than the original.