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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.

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.

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.

Speech tampering has brought serious problems to speech security. Information hiding method can be used for tampering detection if it can satisfy several competitive requirements, e.g., inaudibility, robustness, blindness, and fragility. According to preliminary analysis, spectral envelope and formants are important indicators of tampering, since tampering the speech will unavoidably modify the shape of the spectral envelope and the locations/magnitudes of the formants. By taking advantage of this, this paper proposes a spectral manipulations based information hiding method for tampering detection in sparse domain. To robustly extract the embedded information, the Robust Principal Component Analysis (RPCA) is employed to decompose the original speech into sparse component and low-rank component. The sparse component which contains the main spectral envelope and formant structure is selected for information hiding/embedding via spectral manipulations, by controlling the shape and power of formants with line spectral frequencies (LSFs). Evaluation results suggest that the proposed method can satisfy inaudibility, robustness, and fragility. Furthermore, it is able to detect both the temporal tampering and acoustic feature based tampering with reasonable accuracy.

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.

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.

There have been serious issues concerning the protection of speech signals from malicious tampering. Digital watermarking has been paid much attention in solving this problem. This paper proposes a tampering detection approach based on speech watermarking by modifying the line spectral frequencies (LSFs). Watermarks are embedded into LSFs that derived from linear prediction (LP) analysis with dither modulation-quantization index modulation (DM-QIM). Minor modifications to LSFs introduced by quantization not only enable the watermarks to be inaudible to human auditory system but also provide the possibility of robustness against meaningful processing and fragility against tampering. We evaluated the proposed approach with objective evaluations with respect to inaudibility, robustness, and fragility. The results indicated that the proposed approach for tampering detection not only satisfied inaudibility but also provided good robustness against meaningful processing and fragility against malicious tampering.

Parameters of geomorphological characteristics are critical for research on yardangs. However, methods which are low-cost, accurate, and automatic or semi-automatic for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R2 = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R2 = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23% with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138).

Automatic design of mechanical procedures solving abstract problems is a relevant scientific challenge. In particular, automatic design of membranes systems performing some prefixed tasks is an important and useful research topic in the area of Natural Computing. In this context, deterministic membrane systems were designed in order to capture the values of polynomials with natural numbers coefficients. Following that work, this paper extends the previous result to polynomials with integer numbers coefficients.Specifically,a deterministic transitionP system using priorities in the weak interpretation, associated with an arbitrary such kind polynomial, is presented. The configuration of the unique computation of the system will be encoded by means of two distinguished objects, the values of the polynomial for natural numbers. The descriptive computational resources required by the designed membrane system are also analyzed. Scientific Research Fund of Sichuan Provincial Science and Technology Department 2015JY0257 Scientific Research Fund of Sichuan Provincial Science and Technology Department 2017FZ0010 National Natural Science Foundation of China 61672437 National Natural Science Foundation of China No 61702428 Sichuan Science and Technology Program 2018GZ0185 Sichuan Science and Technology Program 2018GZ0085 Sichuan Science and Technology Program 2017GZ0159

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.

Automatic design of P systems is an attractive research topic in the community of membrane computing. Differing from the previous work that used evolutionary algorithms to fulfill the task, this paper presents the design of a (deterministic transition) P system (without input membrane) of degree 1, capturing the value of an arbitrary k-order (k 2) polynomial p(n) by using a reasoning method. Specifically, the values of p(n) corresponding to a natural number t is equal to the multiplicity of a distinguished object of the system (the output object) in the configuration at instant t. We also discuss the descriptive computational resources required by the designed k-order polynomial P system. Ministerio de Economía y Competitividad TIN2012-37434

This paper proposes a method to realize the lifting scheme of tight frame wavelet filters. As for 4-channel tight frame wavelet filter, the tight frame transforms’ matrix is 2×4, but the lifting scheme transforms’ matrix must be 4×4. And in the case of 3-channel tight frame wavelet filter, the transforms’ matrix is 2×3, but the lifting scheme transforms’ matrix must be 3×3. In order to solve this problem, we introduce two concepts: transferred polyphase matrix for 4-channel filters and transferred unitary matrix for 3-channel filters. The transferred polyphase matrix is symmetric/antisymmetric. Thus, we use this advantage to realize the lifting scheme.

Robust Principal Component Analysis is generalized to a multi-objective optimization problem, named as Multi-objective Robust Principal Component Analysis (MRPCA) in this paper. We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve MRPCA problem, which is a high dimension convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. Since this problem is convex, which is a friendly structure, we propose a modified NSGA-II by introducing a new mutation method: ADMM (Alternating Direction Method of Multipliers) mutation. Numerical experiments show our modified NSGA-II algorithm converges much faster than the standard one.