The use of weak magnetic detection technology to identify defect sizes in ferromagnetic materials poses significant challenges due to the large volume of data and the relatively low prediction accuracy of conventional methods. Therefore, this paper proposes an improved Residual Networks (ResNet18) model that integrates transfer learning and channel attention mechanisms. Compared to traditional methods, the following improvements have been made: First, transfer learning has significantly reduced the data volume and time cost required for training from scratch, enhancing the model's generalization capability. Second, we have added a channel attention mechanism to the ResNet18 model, which involves calculating the importance of each channel through adaptive average pooling and fully connected layers, and generating channel weights using a Sigmoid function. This improvement allows the model to more accurately focus on features with higher relevance to defect sizes. Experimental results demonstrate that for grayscale images with defect lengths of 50 mm, depths of 2 mm, and widths of 1, 2, 3, 4, and 5 mm, the prediction accuracies reached 100 %, 100 %, 98.84 %, 99.58 %, and 100 %, respectively. For grayscale images with defect lengths of 50 mm, widths of 2 mm, and depths of 1, 2, 3, 4, and 5 mm, the prediction accuracies were 99.68 %, 100 %, 99.63 %, 100 %, and 99.60 %, respectively. Compared to the traditional ResNet18 model, the improved model not only enhances the accuracy of defect size prediction but also exhibits greater robustness, providing a new and effective method for defect classification in weak magnetic detection of ferromagnetic materials.

To take the advantages of the remote detection distance and wide detection range from Frequency Modulated Continuous Wave (FMCW) LiDAR system, this paper proposes a novel optoelectronic front end circuit consisting of a balanced-detection (BD) based interface and an Analog Front-End (AFE) circuit. The BD based interface adopts a coupler and differential photodiodes to reduce parasitics and noise, which extends the bandwidth and dynamic range of AFE circuit. The AFE circuit employs a three-stage fully differential shunt-feedback pre-transimpedance amplifier to reduce input impedance and a two-stage variable gain post-amplifier to enhance gain regulation range, which further improve the bandwidth and dynamic range. The proposed AFE circuit in a 40-nm CMOS has achieved a gain of 100 dBΩ from 1 MHz to 132 MHz and a dynamic range up to 66.2 dB. The input-referred noise current is 5.8 pA/√Hz and the power consumption is 11.64 mW at 1.1 V. Compared with the state-of-art designs, the proposed AFE circuit has achieved 1.2× bandwidth expansion and a considerably-improved dynamic range.

Singing voice separation (SVS) and speaker identification (SI) are two classic problems in speech signal processing. Deep neural networks (DNNs) solve these two problems by extracting effective representations of the target signal from the input mixture. Since essential features of a signal can be well reflected on its latent geometric structure of the feature distribution, a natural way to address SVS/SI is to extract the geometry-aware and distribution-related features of the target signal. To do this, this work introduces the concept of optimal transport (OT) to SVS/SI and proposes an improved optimal transport kernel embedding (iOTKE) to extract the target-distribution-related features. The iOTKE learns an OT from the input signal to the target signal on the basis of a reference set learned from all training data. Thus it can maintain the feature diversity and preserve the latent geometric structure of the distribution for the target signal. To further improve the feature selection ability, we extend the proposed iOTKE to a gated version, i.e., gated iOTKE (G-iOTKE), by incorporating a lightweight gating mechanism. The gating mechanism controls effective information flow and enables the proposed method to select important features for a specific input signal. We evaluated the proposed G-iOTKE on SVS/SI. Experimental results showed that the proposed method provided better results than other models.

Learning effective vocal representations from a waveform mixture is a crucial but challenging task for deep neural network (DNN)-based singing voice separation (SVS). Successful representation learning (RL) depends heavily on well-designed neural architectures and effective general priors. However, DNNs for RL in SVS are mostly built on generic architectures without general priors being systematically considered. To address these issues, we introduce deep unfolding to RL and propose two RL-based models for SVS, deep unfolded representation learning (DURL) and optimal transport DURL (OT-DURL). In both models, we formulate RL as a sequence of optimization problems for signal reconstruction, where three general priors, synthesis, non-negative, and our novel analysis, are incorporated. In DURL and OT-DURL, we take different approaches in penalizing the analysis prior. DURL uses the Euclidean distance as its penalty, while OT-DURL uses a more sophisticated penalty known as the OT distance. We address the optimization problems in DURL and OT-DURL with the first-order operator splitting algorithm and unfold the obtained iterative algorithms to novel encoders, by mapping the synthesis/analysis/non-negative priors to different interpretable sublayers of the encoders. We evaluated these DURL and OT-DURL encoders in the unsupervised informed SVS and supervised Open-Unmix frameworks. Experimental results indicate that (1) the OT-DURL encoder is better than the DURL encoder and (2) both encoders can considerably improve the vocal-signal-separation performance compared with those of the baseline model.

Audio watermarking has been developed to protect the copyright of audio signals. We considered the use of the distribution of the phase spectrum and propose an effective multi-bit audio watermarking method based on phase shifting. The proposed method is implemented on the basis of a frame-wise framework. The phase spectrum of each frame is first calculated, then, in accordance with the embedded bit number of each frame, the phase bins are segmented into several subsets. An artificial phase pattern is constructed in one of these subsets to carry a specific multi-bit watermark. In the water-mark extraction process, each frame is synchronized by identifying the phase pattern, then watermarks are extracted. We investigate the performance of the proposed method, and the results indicated that the proposed method balances the trade-off of inaudibility and robustness. From comparative evaluations, the proposed method also performed better than several other audio watermarking methods.

Generative adversarial networks (GANs) and Conditional GANs (cGANs) have recently been applied for singing voice extraction (SVE), since they can accurately model the vocal distributions and effectively utilize a large amount of unlabelled datasets.However, current GANs/cGANs based SVE frameworks have no explicit mechanism to eliminate the mutual interferences between different sources.In this work, we introduce a novel 'crossfire' criterion into GANs to complement its standard adversarial training, which forms a dual-objective GANs, namely Crossfire GANs (Cr-GANs).In addition, we design a Generalized Projection Method (GPM) for cGANs based frameworks to extract more effective conditional information for SVE.Using the proposed GPM, we extend our Cr-GANs to conditional version, i.e., Crossfire Conditional GANs (Cr-cGANs).The proposed methods were evaluated on the DSD100 and CCMixter datasets.The numerical results have shown that the 'crossfire' criterion and GPM are beneficial to each other and considerably improve the separation performance of existing GANs/cGANs based SVE methods.

Monaural singing voice separation (MSVS) is a challenging task and has been extensively studied. Deep neural networks (DNNs) are current state-of-the-art methods for MSVS. However, they are often designed manually, which is time-consuming and error-prone. They are also pre-defined, thus cannot adapt their structures to the training data. To address these issues, we first designed a multi-resolution convolutional neural network (CNN) for MSVS called multi-resolution pooling CNN (MRP-CNN), which uses various-sized pooling operators to extract multi-resolution features. We then introduced Neural Architecture Search (NAS) to extend the MRP-CNN to the evolving MRP-CNN (E-MRP-CNN) to automatically search for effective MRP-CNN structures using genetic algorithms optimized in terms of a single objective taking into account only separation performance and multiple objectives taking into account both separation performance and model complexity. The E-MRP-CNN using the multi-objective algorithm gives a set of Pareto-optimal solutions, each providing a trade-off between separation performance and model complexity. Evaluations on the MIR-1 K, DSD100, and MUSDB18 datasets were used to demonstrate the advantages of the E-MRP-CNN over several recent baselines.

Echo‐hiding has been widely studied for audio watermarking. This study proposes a more secure echo‐hiding method based on modified pseudo‐noise (PN) sequence and robust principal component analysis (RPCA). In the proposed method, the RPCA is used to decompose the original audio signal into low‐rank and sparse parts and then a pair of opposite modified PN sequences is employed to embed watermarks. The modified PN sequence improves the robustness of watermark detection by providing additional correlation peaks. Meanwhile, benefit from the RPCA and the opposite PN sequences, the security of the proposed method is improved since watermarks cannot be detected from the whole signal even if the PN sequence is known, which is an obvious improvement compared with the previous PN‐based echo‐hiding methods. In the watermark detection process, the authors make use of the low‐rank and sparse characteristics of the watermarked signal to detect watermarks from the low‐rank and sparse parts, respectively. Based on this basic framework, they also propose a multi‐bit embedding scheme, which obtains a doubled embedding capacity compared with the previous PN‐based echo‐hiding methods. The proposed method was evaluated with respect to inaudibility, security, and robustness. The experiment results verified the effectiveness of the proposed method.

Audio watermarking plays an important role in copyright protection. Echo hiding, one of the most effective techniques for audio watermarking, has been studied for decades. However, the conventional echo hiding has been criticized for its weak security, as watermarks can be easily extracted by means of cepstrum analysis even without any prior knowledge. This article explores the time-frequency (T-F) characteristics of the repetition structures in an audio signal to improve the security of conventional echo hiding. In our approach, the original audio signal is first converted into a high-dimensional T-F representation, and then by clustering the time frames that have similar T-F characteristics into the same subspace, the original audio is decomposed into a union of subspaces with each corresponding to one time-domain subsignal. Paired and opposite echo kernels are applied to energy-balanced subsignals for watermark embedding, which significantly improves the security. In the watermark extraction process, the subspaces are recovered on the basis of the T-F similarities and cepstrum analysis is utilized to extract watermarks. The proposed embedding and extraction schemes thus offer a new approach for echo hiding. The results of experiments demonstrate the effectiveness of our approach with respect to inaudibility, security, and robustness.

The method reported here realizes an inaudible echo-hiding based speech watermarking by using sparse subspace clustering (SSC). Speech signal is first analyzed with SSC to obtain its sparse and low-rank components. Watermarks are embedded as the echoes of the sparse component for robust extraction. Self-compensated echoes consisting of two independent echo kernels are designed to have similar delay offsets but opposite amplitudes. A one-bit watermark is embedded by separately performing the echo kernels on the sparse and low-rank components. As a result, the sound distortion caused by one echo signal can be quickly compensated by the other echo signal, which enables better inaudibility. Since the embedded echoes have the same sparsity as the sparse component, watermarks can be extracted with a basic cepstrum analysis even if the echo kernels are not directly performed on the original speech. The evaluation results verify the feasibility and effectiveness of this method.

The recent deep learning methods can offer state-of-the-art performance for Monaural Singing Voice Separation (MSVS). In these deep methods, the recurrent neural network (RNN) is widely employed. This work proposes a novel type of Deep RNN (DRNN), namely Proximal DRNN (P-DRNN) for MSVS, which improves the conventional Stacked RNN (S-RNN) by introducing a novel interlayer structure. The interlayer structure is derived from an optimization problem for Monaural Source Separation (MSS). Accordingly, this enables a new hierarchical processing in the proposed P-DRNN with the explicit state transfers between different layers and the skip connections from the inputs, which are efficient for source separation. Finally, the proposed approach is evaluated on the MIR-IK dataset to verify its effectiveness. The numerical results show that the P-DRNN performs better than the conventional S-RNN and several recent MSVS methods.

Random mixing and circularly shifting for augmenting the training set are used to improve the separation effect of deep neural network (DNN)-based monaural singing voice separation (MSVS). However, these manual methods are based on unrealistic assumptions that two sources in the mixture are independent of each other, which limits the separation effect. This paper proposes a data augmentation method based on variational autoencoder (VAE) and generative adversarial network (GAN), which is called as VAE-GAN. The VAE models the observed spectra of sources (vocal and music) separately and reconstructs new spectra from the latent space. The GAN's discriminator is introduced to measure the correlation between the latent variables of the vocal and music generated by the VAE probability encoder. This adversarial mechanism in VAE's latent space could learn the synthetic likelihood and ultimately decode high quality spectra samples, which further improves the separation effect of general MSVS networks.

This work proposes a simple but effective attention mechanism, namely Skip Attention (SA), for monaural singing voice separation (MSVS). First, the SA, embedded in the convolutional encoder-decoder network (CEDN), realizes an attention-driven and dependency modeling for the repetitive structures of the music source. Second, the SA, replacing the popular skip connection in the CEDN, effectively controls the flow of the low-level (vocal and musical) features to the output and improves the feature sensitivity and accuracy for MSVS. Finally, we implement the proposed SA on the Stacked Hourglass Network (SHN), namely Skip Attention SHN (SA-SHN). Quantitative and qualitative evaluation results have shown that the proposed SA-SHN achieves significant performance improvement on the MIR-1K dataset (compared to the state-of-the-art SHN) and competitive MSVS performance on the DSD100 dataset (compared to the state-of-the-art DenseNet), even without using any data augmentation methods.

This paper proposes a watermarking method for speech signals based on Robust Principal Component Analysis (RPCA) and formant manipulations. As the spectrogram of speech has a relatively sparse structure, the core information of speech is extracted into a sparse matrix using RPCA so that formants can be estimated with Linear Prediction (LP) more accurately even under noise/interferences, which significantly improves the robustness of proposed method. We investigate how the formants can be controlled and manipulated to make the watermarking method effective. Watermarks are embedded into speech by controlling the shape and power of formants using the stable and robust parameter, i.e., line spectral frequencies (LSFs). Evaluations regarding inaudibility and robustness are carried out and the results suggest that the proposed method can not only satisfy inaudibility but also provide good robustness against general processing and different speech codecs which is better than the other methods.