2026· IEEE Signal Processing Letters· Vol 33, pp. 2959-2963· 0 citations· 24 references
Abstract
This letter proposes GC-SQMCC, a computationally efficient robust adaptive filtering algorithm tailored for fixed-point acoustic echo cancellation (AEC) under impulsive noise. Departing from signal-driven quantizers, the proposed scheme synergistically aligns a nonlinear compression-mapping quantizer with the influence function of the generalized Cauchy (GC) kernel, allocating fine resolution near zero and gracefully coarsening for large impulses. An adaptive error envelope tracker (AEET) normalizes the dynamic range online, while a synergistic look-up table (LUT) fuses the kernel weighting and error scaling into a single memory access, enabling 16-bit integer arithmetic during the per-sample filtering stage by moving divisions and transcendental operations to the infrequent LUT-update process. A threshold-based adaptive LUT update (ALUT-U) further decouples high-frequency filtering from low-frequency grid adjustment. Mean and mean-square stability conditions are derived, and an asymptotic steady-state MSD is derived under high-resolution assumptions. Simulations on AEC under single-talk and double-talk scenarios show that GC-SQMCC achieves lower steady-state MSD than competing quantized baselines under various configurations.
Existing lightweight acoustic echo cancellation (AEC) systems often combine linear AEC with Bark-domain DNN-based suppression to lower the computational footprint. In such systems, downsampling layers further compress the input features into a compact bottleneck representation, but this compression weakens frequency-time modeling capacity and degrades performance. To mitigate this limitation, we propose MSA-EchoLite, a lightweight Bark-domain AEC framework with an asymmetric dual-branch encoder and an echo-aware frequency-time modulation (EAM) module. The EAM module enriches the compressed bottleneck representation by modeling discrepancy and correlation cues between the dual-branch microphone and echo-related latent features. Experimental results show that the Bark-domain variant of MSA-EchoLite offers a better performance-complexity trade-off than its frequency-domain counterpart but is more sensitive to feature compression. With only 26.1% additional FLOPs over its non-EAM Bark-domain variant, its EAM-enhanced version achieves 99.1% of the PESQ of the frequency-domain counterpart, which requires nearly twice the FLOPs, and even surpasses it in SDR. Overall, MSA-EchoLite outperforms state-of-the-art lightweight AEC models while using only 0.2 M parameters and 100 M FLOPs/s.
Ye Ni, Ruiyu Liang, Qingyun Wang et al.· 0 citations
While the recursive least square (RLS) algorithm is widely used in adaptive filtering applications like acoustic echo cancellation (AEC) due to its fast convergence rate, its high computational complexity severely limit its practical deployment for long filters. In this paper, a regularized block-diagonal RLS (RBD-RLS) algorithm is proposed to address these challenges. By approximating the inverse covariance matrix as a block-diagonal structure, RBD-RLS simplifies the update process into independent parallel computations of sub-blocks, effectively reducing the computational complexity. Additionally, Tikhonov regularization is applied to each sub-blocks for enhance numerical stability. A series of experimental results demonstrate that RBD-RLS maintains good convergence while significantly reducing computational complexity. Moreover, it still exhibits relative robustness in real-world scenarios.
A feedback-guided DNN-based controller fusion framework for robust fixed-parameter ANC that combines a causal WaveNet controller with a feedback-guided mixture-of-experts (MoE) module, where a gating network estimates the weights of multiple pre-trained FIR experts according to the current acoustic condition.
Lu Bai, Yiming He, Xiaofeng Nan et al.· 0 citations
Joint frequency and chirp-rate estimation for a noisy chirp signal arises in radar, sonar, and burst satellite communications. Conventional estimators combine a coarse grid search with fine interpolation; accuracy degrades at the edges of the residual cell (the edge effect) and below the breakdown SNR (the threshold effect). We present a deterministic two-stage estimator that controls both failure modes uniformly over the residual cell. The estimator combines a time-centered, zero-padded dechirp-FFT acquisition bank with alternating selectable-$p$ amplitude-interpolation refinements on DTFT samples at fractional bins; in the centered frame, the frequency-chirp-rate cross-term of the Fisher information vanishes. The paper derives a mean-squared-error and threshold characterization over the full SNR range, in closed form except for one calibrated scalar (an effective cell count), to our knowledge the first for the joint problem: the breakdown threshold is governed by the cell count, and its cell-position dependence is dominated by the scalloping loss of the coarse FFT, which the padding bounds at 0.4 dB. An asymptotic uniformity analysis over the cell, including its corners, gives fixed-point variance ratios of $1.003$ and $0.998$, analytically free of the residual. A closed-form bias analysis under a cubic phase mismatch shows the centered chirp-rate estimate is insensitive to first order. Monte Carlo experiments at $N=256$ (validated at $N=32$-$512$) measure frequency- and chirp-rate-axis efficiencies with median $1.03$ and worst case $1.07$ over $144$ cell positions at $-5$ dB. Threshold predictions hold within $1.0$ dB on four configurations not used in the calibration. The dechirp-FFT bank is fully parallel, and each of the four refinement iterations evaluates three DTFT samples per axis; under fixed operating conditions, per-estimate latency is constant at $O(N\log N)$ cost.
Miao-Miao Wei, Jianjun Li, Yang Wang et al.· 0 citations
Polyphase filter banks (PFBs) are widely used to channelize digitized time-domain data in real time. However, applications such as radio astronomy often require higher spectral resolution than real-time systems can provide. We introduce a circulant inverse-PFB formalism and present a new, scalable, fast Fourier transform (FFT)-based algorithm for inverting critically sampled PFBs. This formalism explicitly identifies the origin of quantization noise amplification and enables two complementary mitigation strategies: (1) a fast, practical Wiener filter and (2) a rigorous maximum-likelihood reconstruction with time-domain priors. We evaluate both approaches using simulated 4-bit quantized PFB data. For autospectra, Wiener filtering confines reconstruction errors to less than 10\% of the channel width, with a peak error below 10\%. The maximum-likelihood approach further reduces reconstruction errors as additional time-domain prior information is incorporated, with priors spanning 10\% of the time-domain samples reducing worst-case errors to below 2\%. This framework enables high-resolution spectral analysis of archival PFB data, facilitating applications including long-baseline interferometry, pulsar and fast radio burst searches, and ultra-narrow-band physics experiments.
S. Fay, Mohan Agrawal, H. Chiang et al.· 0 citations
Passive acoustic localization in complex shallow waters requires algorithms tailored to specific operational constraints. This paper investigates the adaptability, computational efficiency, and statistical performance boundaries of five matched-field processing (MFP) methods—Bartlett, Minimum Variance Distortionless Response (MVDR), Multiple Signal Classification (MUSIC), Reduced Covariance Matrix (RCM), and Rank and Trace Minimization (RTM)—using the Elba-93 sea trial dataset. Error metrics and processing complexities are systematically evaluated across stationary and maneuvering target scenarios. Rigorous non-parametric statistical tests reveal distinct operational boundaries: under stationary conditions dominated by systemic environmental mismatch, energy-based processors guarantee reliable baseline stability. Conversely, under snapshot-deficient dynamic conditions tracking a receding target, standard high-resolution subspace methods become highly vulnerable to trajectory jumps. In such highly dynamic scenarios, adaptive energy-based processors (specifically MVDR) exhibit the most stable tracking continuity and lowest numerical peak errors. Simultaneously, the operational adaptability of subspace methods is improved via covariance matrix reconstruction (CMR). Specifically, the RCM technique effectively decouples unstructured sensor noise, mitigating maximum trajectory deviations and providing a balanced trade-off between computational efficiency and robustness. Statistical evaluations confirm the fundamental performance boundaries in static environments, while highlighting sample-size limitations in highly dynamic scenarios, thereby establishing a realistic, evidence-based benchmark for marine engineering applications.
Zikun Meng, Wen Zhang, Jian Shi et al.· Journal of Marine Science an...· 0 citations