Suppressing broadband low-frequency sound has great scientific and engineering significance. However, normal porous acoustic materials backed by a rigid wall cannot really play its deserved role on low-frequency sound absorption. Here, we demonstrate that an ultrathin sponge coating can achieve high-efficiency absorptions if backed by a metasurface with moderate surface impedance. Such a metasurface is constructed in a wide frequency range by integrating three types of coiled space resonators. By coupling an ultrathin sponge coating with the designed metasurface, a deep-subwavelength broadband absorber with high absorptivity ( $${>}80\%$$> 80 % ) exceeding one octave from 185 Hz to 385 Hz (with wavelength $$\lambda $$λ from 17.7 to 8.5 times of thickness of the absorber) has been demonstrated theoretically and experimentally. The construction mechanism is analyzed via coupled mode theory. The study provides a practical way in constructing broadband low-frequency sound absorber.

Independent vector analysis (IVA) and tensor decomposition are two types of effective algorithms for joint blind source separation (JBSS) with different statistical assumptions. Although IVA and tensor decomposition are intrinsically linked, their explicit connection has not been reported. In this letter, we reveal their explicit connection through a piecewise stationary multivariate complex Gaussian signal model. With this model, IVA can be explained as reconstructing the covariances of the mixtures in a similar manner as double coupled canonical polyadic decomposition (DC-CPD), a typical tensor-based algorithm, with the only difference being the distance metric used in the cost function. Numerical experiments show that IVA can achieve better separation performance but is highly dependent on how well the a priori model matches the actual signal, while DC-CPD is more robust to the model mismatch.

Ship-radiated noise (SRN) recognition is vital for underwater acoustics, with applications in both military and civilian fields. Traditional manual recognition by sonar operators is inefficient and error-prone, motivating the development of automated recognition systems. However, most existing deep learning approaches demand high computational resources, limiting their deployment on resource-constrained edge devices. To overcome this challenge, we propose LW-MS-LFTFNet, a lightweight model informed by time-frequency analysis of SRN that highlights the critical role of low-frequency components. The network integrates a multi-scale depthwise separable convolutional backbone with CBAM attention for efficient spectral representation, along with two LSTM-based modules to capture temporal dependencies in low-frequency bands. Experiments on the DeepShip dataset show that LW-MS-LFTFNet achieves 75.04% accuracy with only 0.85 M parameters, 0.38 GMACs, and 3.27 MB of storage, outperforming representative lightweight architectures. Ablation studies further confirm that low-frequency temporal modules contribute complementary gains, improving accuracy by 2.64% with minimal overhead. Guided by domain-specific priors derived from time-frequency pattern analysis, LW-MS-LFTFNet achieves efficient and accurate SRN recognition with strong potential for edge deployment.

Adaptive algorithm based on multi-channel linear prediction (MCLP) is an effective dereverberation method. However, the abrupt change of the target speech source position makes it difficult to guarantee both the fast convergence speed and the optimal steady-state behavior. In this letter, the recursive-least-squares (RLS)-based and Kalman-filter-based adaptive MCLP method for speech dereverberation are investigated. Based on the relative weighted change of the adaptive filter coefficients, a time-varying forgetting factor for the RLS algorithm and a re-initialization mechanism for the Kalman filter are proposed to make the algorithm robust to the abrupt change of the target speaker positions. The advantages of the proposed scheme are demonstrated in the experiments.

The highly imbalanced power spectral density of full-band speech signals poses a significant challenge to full-band speech enhancement, and the commonly used spectral features that mimic the behavior of the human auditory system are not an optimal choice for full-band speech enhancement. In this paper, a learnable spectral dimension compression mapping is proposed to effectively compress the spectral feature along frequency, preserving high resolution in low frequencies while compressing information in high frequencies in a more flexible manner. Experimental results verify that the proposed method can be easily combined with different full-band speech enhancement models and achieve better performance.

The multichannel variational autoencoder (MVAE) integrates the rule-based update of a separation matrix and the deep generative model and proves to be a competitive speech separation method. However, the output (global) permutation ambiguity still exists and turns out to be a fundamental problem in applications. In this paper, we address this problem by employing two dedicated encoders. One encodes the speaker identity for the guidance of the output sorting, and the other encodes the linguistic information for the reconstruction of the source signals. The instance normalization (IN) and the adaptive instance normalization (adaIN) are applied to the networks to disentangle the speaker representations from the content representations. The separated sources are arranged in designated order by a symmetric permutation alignment scheme. In the experiments, we test the proposed method in different gender combinations and various reverberant conditions and generalize it to unseen speakers. The results validate its reliable sorting accuracy and good separation performance. The proposed method outperforms the other baseline methods and maintains stable performance, achieving over 20 dB SIR improvement even in high reverberant environments.