Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment

Acoustic bird species classification under low SNR and small-scale dataset conditions

This study proposes a bi-directional recurrent neural network (Bi-RNN) post-processing method for speech enhancement (SE) at low signal-to noise ratios (SNR). Current speech enhancement solutions performed badly under low SNR situations. Loizou and Kim proposed a solution to reduce speech distortion errors in time-frequency (T-F) domain but it requires the knowledge of ground truth. As ground truth is unknown in real-life applications, the current study proposes to use a Bi-RNN to implement Loizou and Kim’s solution as a post-processing method for SE engines. Our solutions do not require prior knowledge of ground truth. The effectiveness of the proposed method is investigated with a spectral subtraction (SS) SE engine, a non-negative matrix factorization (NMF) SE engine, and a deep neural network ideal ratio mask (DNN-IRM) SE engine, under matched/mis-matched noise and different SNR conditions. Experimental results demonstrate that the proposed post-processing method effectively improved both perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) for all of these SE engines, especially at low SNR conditions.

A precise estimation of SFO (Sampling Frequency Offset) is an important issue in OFDM (Orthogonal Frequency Division Multiplexing) system. However, the conventional method causes serious performance degradation of SFO estimation in low SNR (Signal to Noise Ratio) or large Doppler frequency environment. Therefore, in this paper, we propose a SFO estimation method which can achieve stable operation in low SNR and large Doppler frequency environment. The proposals for SFO estimation /compensation are mainly based on DVB (Digital Video Broadcasting) system, and we verified that the proposed method has a good performance and stable operation through extensive simulation.

A precise estimation and compensation of SFO (Sampling Frequency Offset) is an important issue in OFDM (Orthogonal Frequency Division Multiplexing) system, because sampling frequency mismatch between the transmitter and the receiver dramatically degrades the system performance due to the loss of orthogonality between the subcarriers. However, the conventional method causes serious performance degradation of SFO estimation in low SNR (Signal to Noise Ratio) or large Doppler frequency environment. Therefore, in this paper, we propose two SFO estimation methods which can achieve stable operation in low SNR and large Doppler frequency environment. The proposals for SFO estimation / compensation are mainly specialized on DVB (Digital Video Broadcasting) system, and we verified that the proposed method has good performance and stable operation through extensive simulation.

In the last few years, the demand of robust beamforming techniques has been increased significantly to serve numerous purposes. However, beamforming under low Signal to Noise Ratio (SNR) condition has always been a challenging task, which becomes more tedious in case of multiple sources. On the other hand, there are numerous factors such as steering vector error or similar estimation error problems that confine the efficiency of major beamforming techniques. Classical methods employ Eigen-Value Decomposition (EVD) and Singular Value Decomposition (SVD) over covariance matrix of the received signal; however their efficiency is limited under low SNR and multi-source condition, and perform inferiorly in case of steering vector errors. Such approaches are limited to achieve optimal Direction-of-Arrival (DOA) estimation that degrades the overall performance of the beamformer. Considering all these limitations, in this research, the focus is made on designing a robust DOA estimation model, followed by a novel beamformer for 8-element Uniform Linear Array (ULA). The simulation work consists two parts DOA estimation and beamforming. Unlike classical MUSIC and ESPRIT algorithms popular for DOA estimation our proposed model exploits the propagation method based subspace method where at first covariance matrix is obtained for the received signal, which is followed by normalization and Cross-Correlation matrix estimation. The obtained correlation matrix is processed for Generalized Eigen-Value Decomposition (GEVD) that efficiently obtained (orthogonal) subspace and SOI separation to yield DOA information. This approach enables more efficient DOA estimation even under low-SNR and steering vector error conditions. After obtaining DOA information, a novel adaptive beamformer is designed by using MVDR and FROST in parallel. DOA information from proposed model is given as input to the adaptive MVDR FROST beamforming model. Exploiting dynamic Signal-to-Interference ratio (SINR) by each beamformer our proposed method introduces an adaptation model that intends to achieve higher SINR value. MATLAB 2018a based simulation confirmed the robustness of the proposed model in terms of DOA estimation and beamforming results.

In this paper, a robust multiple observation likelihood ratio test (MOLRT) based voice activity detection (VAD) method is proposed. At the beginning of this paper, we introduce the Wiener filter to the observed signal in time domain which can help mitigating some noise. The reason why we use the Wiener filter for VAD is that the performance of the VAD is always better in high signal to noise ratio (SNR) range than in low SNR range. Then, some ideas are proposed to improve the performance of the MOLRT based VAD method. As we all know, a conventional MOLRT based method including three modules named likelihood ratio (LR) estimation, threshold setting and hangover technique. To improve the estimation accuracy of LR, we adopt the unbiased minimum mean-square error (MMSE) algorithm for noise power spectrum estimation in every frame, which is very effective for LR estimation in MOLRT-based VAD method. That is because the LR is a function of a prior and a posterior SNR and unbiased MMSE algorithm is very useful for noise estimation. In addition, to make our VAD method more robust, a dynamic threshold setting technique is proposed in our method, which is related to the minimum noise power spectrum. That is because minimum noise power spectrum can help us updating the value of threshold to a suitable level according to the denoised signal. Last but most important, a novel hangover algorithm is introduced in this paper comparing to the conventional HMM based hangover algorithm. In the novel hangover algorithm, the current frame is determined by the statistical result of the following speech/non-speech detections based on the likelihood ratio test. And the evaluation results reveal that proposed method significantly outperform the baseline result of LRT as regards VAD accuracy in both noise variations and low SNR conditions.

The research difficulties of speech detection in the present focus on the cases that the signal-to-noise ratio(SNR) is low and the background noise changes dramatically. For the problem of speech detection under low SNR environments, based on the sparsity of speech in frequency domain and the sparse representation ability in frequency domain of the over-complete Fourier basis, the speech signal is reconstructed with Matching Pursuit algorithm, and we propose a low SNR speech detection method which uses the short time energy of the reconstructed signal as a detection feature. The experimental results show that this algorithm exhibits higher robustness in the low SNR white noise environments.

Generally, the performance of endpoint detection is affected by the noise. In this paper, we propose a novel two-layer decision model based on noise classification to detect the activity voice robustly. The training processing mainly contains two steps: firstly, we employ the noisex-92 database, which consists of different types of pure noise, to train a BP neural network in order to classify the noise type precisely, secondly, we train BP neural networks for each noise type covering large range of signal noise ratio (SNR). In the testing phase, we assume that the short period of silence at the beginning of the signal contains features for noise and utilize them to get the noise type. Then, we use the classifier corresponding to the noise type to detect activity voice. We conduct experiments on TIMIT corpus for 5 noise types under 7 SNR conditions. And experimental results show that our method outperforms global classifier, especially in low SNR condition.

To improve the accuracy and reliability of modulation recognition at low signal to noise ratio (SNR) and few knowledge of signal parameter, the novel method based on the cyclic spectral feature and support vector machine(SVM) is presented. In the process of novel algorithms, the cyclic spectral analysis is used to realize the feature extract of the modulated signals, and the Eigenface method is used to reduce the amount of spectral coherence feature. Then, a new scheme of classification based on support vector machine is presented to classify the modulation signal. The experiment shows that the modulation classification accuracy of presented method is significantly improved at low SNR environment.

Low signal-to-noise ratio (SNR) low probability of intercept (LPI) radar signal recognition is a key technical challenge in the field of electronic reconnaissance. Under low SNR conditions, severe noise contamination hinders the extraction of time-frequency features, resulting in degraded performance for traditional recognition methods. To address this issue, this paper proposes a cascaded framework, named MAE-WGAN, which integrates a Multi-Attention Enhanced Wasserstein Generative Adversarial Network for time-frequency image denoising, followed by radar signal recognition using a Convolutional Neural Network (CNN). The proposed approach embeds channel, spatial, and self-attention mechanisms into the U-Net-based generator of WGAN, forming a multi-stage radar signal processing system. First, under the guidance of high-quality time-frequency images, MAE-WGAN adaptively learns key time-frequency features using the multi-attention modules. Next, a discriminator is trained within the WGAN framework to recover signal structures obscured by noise. Finally, a dedicated multi-level feature extraction network is employed for accurate radar signal classification. Experimental results on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2}$</tex> typical LPI radar signals demonstrate that MAE-WGAN achieves high recognition accuracy under low SNR conditions, exhibiting strong recognition performance and robustness across varying SNR conditions.

We focus on the sound source localization (SSL) problem of small/weak voices recorded by small humanoid robots, such as Nao. The localization of small voice on such robots is more difficult than those with large bodies because 1) the number of microphones and their positions are restricted and 2) the signal to noise ratio (SNR) between internal noise and human speech is low. Multiple Signal Classification (MUSIC) is a promising noise-robust SSL method, and it has several variants in its eigenvector decomposition process, such as generalized eigenvalue decomposition (GEVD-MUSIC) and generalized singular value decomposition (GSVD-MUSIC). However, their performances are seriously affected by noise properties, the number of microphone, and their configurations on each robot's body. Since these have been confirmed only on robots with large bodies and many microphones, we need to investigate which variants perform better on a small humanoid robot with internal noise in it. We furthermore propose another MUSIC based on transformed steering vector (TSV-MUSIC) as a new implementation of GEVD-MUSIC. The computational cost of TSV-MUSIC is reduced compared with GEVD-MUSIC by changing its matrix multiplication procedures. Experimental results using real recoded data showed that our TSV-MUSIC outperformed others in terms of localization correctness by about 10 points under low SNR condition. We also compared the properties and performances of MUSIC-based SSLs by using simulated data.

The acoustic pyrometer system uses physical properties of the gas and registers the perceivable temperature without the effects of radiation, and the measuring accuracy of acoustic wave flight time is the major factor in its application. The traditional correlation algorithm could not overcome convolution interference and calculate the time delay of acoustic signals accurately, under the low SNR (Signal Noise Ratio) and complex reverberation noise conditions. This paper presents a generalized cross power spectrum algorithm to filter the product and convolution interferences, and improves the performances of anti-noise and anti-convolution by whitening the sample signals and adjusting the weighted value of cross power spectrum algorithm with the variation of SNR. The experimental result and theoretical analysis showed that the new generalized cross power spectrum algorithm compared to the traditional correlation function analytical algorithm, can overcome the convolution interferences from reverberation noise, and sharpen peak value, thereby estimate the time delays of signals accurately.

Target vibration introduces unexpected vibration phases in the echo signal of inverse synthetic aperture LADAR (ISAL). The vibration phases cause the imaging results to be defocused. How to estimate the vibration phases has always been one of the difficulties in ISAL imaging, and there has not been an effective solution. In this paper, two methods are proposed to estimate the vibration phases in different situations. The method based on multi-channel along-track interferometry (MCATI method) is for some cases that the target velocity vector is parallel to some baselines of ISAL. The method based on orthogonal interferometry (OI method) is for other cases that the target velocity vector is not parallel to any baseline of ISAL. In addition, a high signal to noise ratio (SNR) is essential for the MCATI and OI methods to get high estimation accuracy. Therefore, a large power-aperture product should be carefully considered in the ISAL system design. Furthermore, on the condition of low SNR, range cell accumulation is also helpful to reduce the influence of noise. Take the ISAL, which is utilized to image the satellite in 500km orbit, as an example, the estimation accuracy can satisfy the demand of synthetic aperture imaging with 0dB single-pulse SNR and 300 range cell accumulation. The proposed methods are verified with simulation and experiment.

Signal time-frequency analysis under low signal-to-noise ratio (SNR) conditions is an important part of signal processing. As Wigner-Ville distribution (WVD) transformation can save all the traits of the original signal in the special space where the frequency is zero, a new method is proposed to extract the signal time-frequency relationship under low SNR conditions and to estimate the extent to improve the SNR after the denoising processing. Through signal time-frequency extracting comparison and parameter extracting comparison, it is proved that this method has higher extraction validity and feasibility.

Recently, microwave imaging (MWI) based on randomized fields has shown promising potential in practical scenes. However, the off-grid error and low signal-to-noise ratio (SNR) measurements remain a challenge for the imaging process. In this paper, we proposed a compressed sensing (CS) based off-grid calibration method for MWI with random illuminations under low SNR environment. A noisy mismatched imaging model with off-grid error is firstly established. Under the framework of CS, the off-grid error is corrected by genetic algorithm (GA) and singular value decomposition (SVD) is conducted to enhance the SNR of echo signal. In the inversion, the OMP algorithm is used due to its high computational efficiency. At last, the effectiveness of the proposed method is verified based on the numerical data. Compared to the existing algorithms, the proposed approach is able to achieve satisfactory results in both off-grid calibration and noise suppression. Besides, the imaging performance versus the number of measurements, SNR, off-grid level and sparsity is discussed. The proposed method provides a new clue for off-grid MWI in low SNR scenarios.