This paper addresses the problem of recovering a synthetic aperture radar (SAR) signal that is corrupted by both radio frequency interference (RFI) and wideband interference (WBI). The time-frequency domain is utilized for both the SAR signal and interference in the form of sparse representations. By doing so, a unified framework that allows one to suppress both the RFI and WBI while recovering the SAR signal can be developed. The resulting framework is an optimization problem that is efficiently solved using a customized alternating direction method of multipliers approach. Finally, simulation results are provided to demonstrate that the performance of the joint estimation algorithm is superior to the performances of other methods in terms of both subjective and objective evaluation standards.

The last decade has seen increased interest in environmental sound classification (ESC) due to the increased complexity and rich information of ambient sounds. The state-of-the-art methods for ESC are based on transfer learning paradigms that often utilize learned representations from common image-classification problems. This paper aims to determine the effectiveness of employing pre-trained convolutional neural networks (CNNs) for audio categorization and the feasibility of retraining. This study investigated various hyper-parameters and optimizers, such as optimal learning rate, epochs, and Adam, Adamax, and RMSprop optimizers for several pre-trained models, such as Inception, and VGG, ResNet, etc. Firstly, the raw sound signals were transferred into an image format (log-Mel spectrogram). Then, the selected pre-trained models were applied to the obtained spectrogram data. In addition, the effect of essential retraining factors on classification accuracy and processing time was investigated during CNN training. Various optimizers (such as Adam, Adamax, and RMSprop) and hyperparameters were utilized for evaluating the proposed method on the publicly accessible sound dataset UrbanSound8K. The proposed method achieves 97.25% and 95.5% accuracy on the provided dataset using the pre-trained DenseNet201 and the ResNet50V2 CNN models, respectively.

The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech sound quality and intelligibility improvements for all noise types and levels when compared with the AC-only beamformer with a postfiltering algorithm.

This paper studies the mean and mean square convergence behaviors of the normalized least mean square (NLMS) algorithm with Gaussian inputs and additive white Gaussian noise. Using the Price’s theorem and the framework proposed by Bershad in IEEE Transactions on Acoustics, Speech, and Signal Processing (1986, 1987), new expressions for the excess mean square error, stability bound and decoupled difference equations describing the mean and mean square convergence behaviors of the NLMS algorithm using the generalized Abelian integral functions are derived. These new expressions which closely resemble those of the LMS algorithm allow us to interpret the convergence performance of the NLMS algorithm in Gaussian environment. The theoretical analysis is in good agreement with the computer simulation results and it also gives new insight into step size selection.

The front-wall clutter removal in the case of the through-the-wall radar (TWR) system is studied in this work. To remove the wall clutter, its low-rank property is utilized, and at the same time, the sparse property of the target returns is exploited to perform target reconstruction. To account for the unparalleled setting of the antenna and the wall, a weighted nuclear norm minimization (WNNM) is employed, and the resulting problem is solved in an alternating manner. In addition, different transmitted waveform signals, including monofrequency and stepped-frequency waveforms, are used to demonstrate their effects on the clutter suppression performances. The experimental results show that the proposed WNNM with stepped-frequency waveform outperforms other approaches.

This paper studies the statistical properties of the gain functions, which are often used for two-channel post-filtering (TC-PF) algorithms. We reveal that the smoothing factor has a significant impact on both noise reduction and musical noise. When the smoothing factor increases, noise reduction can be improved and musical noise can be reduced simultaneously. However, the smoothing factor could not be too close to one because the system can only be assumed to be time-invariant for short durations. To solve this problem, this paper proposes an adaptive smoothing scheme by detecting the sudden change of the system. Moreover, the residual noise floor is adaptively chosen based on the structure of the noise power spectral density (NPSD) to further suppress the tonal noise components. Experimental results show the better performance of the proposed algorithm in terms of the segmental signal to-noise-ratio (SNR) and the PESQ improvements.