Speaker verification using attentive multi-scale convolutional recurrent network

In this paper, we propose a speaker verification method by an Attentive Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local spatial information and global sequential information from the input speech recordings. In the proposed method, logarithm Mel spectrum is extracted from each speech recording and then fed to the proposed AMCRN for learning speaker embedding. Afterwards, the learned speaker embedding is fed to the back-end classifier (such as cosine similarity metric) for scoring in the testing stage. The proposed method is compared with state-of-the-art methods for speaker verification. Experimental data are three public datasets that are selected from two large-scale speech corpora (VoxCeleb1 and VoxCeleb2). Experimental results show that our method exceeds baseline methods in terms of equal error rate and minimal detection cost function, and has advantages over most of baseline methods in terms of computational complexity and memory requirement. In addition, our method generalizes well across truncated speech segments with different durations, and the speaker embedding learned by the proposed AMCRN has stronger generalization ability across two back-end classifiers.Comment: 21 pages, 6 figures, 8 tables. Accepted for publication in Applied Soft Computin

Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet

We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome. First, the audio samples are recorded by different devices, and there is mismatch of recording devices in audio samples. We reduce the negative impact of the mismatch of recording devices by using some effective strategies, including data augmentation (e.g., mix-up, spectrum correction, pitch shift), usages of multi-patch network structure and channel attention. Second, the model size should be smaller than a threshold (e.g., 128 KB required by the DCASE2021 challenge). To meet this condition, we adopt a ResNet with both depthwise separable convolution and channel attention as the backbone network, and perform model compression. In summary, we propose a low-complexity ASC method using data augmentation and a lightweight ResNet. Evaluated on the official development and evaluation datasets, our method obtains classification accuracy scores of 71.6% and 66.7%, respectively; and obtains Log-loss scores of 1.038 and 1.136, respectively. Our final model size is 110.3 KB which is smaller than the maximum of 128 KB.Comment: 5 pages, 5 figures, 4 tables. Accepted for publication in the 16th IEEE International Conference on Signal Processing (IEEE ICSP

Effects of Bias Voltages on the Structural, Mechanical and Oxidation Resistance Properties of Cr–Si–N Nanocomposite Coatings

Cr–Si–N nanocomposite coatings were deposited by multi-arc ion plating under different bias voltages. The influences of bias voltage on composition, microstructure, surface morphology and mechanical properties of Cr–Si–N nanocomposite coatings were investigated in detail. The HR-TEM, XRD, and XPS results confirmed the formation of nanocomposite structure of nanocrystalline of CrN embedded into the amorphous phase of Si3N4. The particle radius of CrN can be calculated from the half-width of the diffraction peak of CrN (200) and the value was about 20–60 nm. In addition, no diffraction peaks of CrSi2, Cr3Si, or Si3N4 were found in all the Cr–Si–N coatings. With the increasing of bias voltages from 0 to −200 V, the number and size of large droplets on the coating surface decreased, and the growth mode of the coatings changed from loose to dense. However, with the increasing of bias voltages from 0 to −200 V, the micro-hardness of the coatings increased and then decreased, reaching its maximum value at negative bias voltages of 100 V. It was found that the friction coefficient of Cr–Si–N coatings is almost the same except for the Cr–Si–N coatings deposited under bias voltage of 0 V. When the oxidation temperature was at 800 °C, the Cr–Si–N coating was only partially oxidized. However, with the increase of oxidation temperature to 1200 °C, the surface of the coating was completely covered by the oxide generated. The results showed that the bias voltages used in multi-arc ion plating had effects on the structure, mechanical, and high temperature oxidation resistance properties of Cr–Si–N nanocomposite coatings

New bioactive derivatives of nonactic acid from the marine Streptomyces griseus derived from the plant Salicornia sp.

Three new compounds, butyl homononactate (5), butyl nonactate (6), 8-actyl homononactic acid (7), along with four known compounds homononactic acids (1), nonactic acid (2), homononactyl nonactate (3), homononactyl homononactate (4) were isolated from the marine Streptomyces griseus RSH0407, derived from the plant Salicornia sp., Chenopodiaceae. Their structures were elucidated by extensive spectroscopic techniques and by comparison with data reported in the literature. The absolute configuration of 3 was first reported by using X-ray copper radiation. Compound 5 exhibited cytotoxic activities against the HCT-8, A2780, BGC-823, BEL-7402, and A549 cell lines in vitro, with IC50 values of 2.87 ± 0.20, 4.90 ± 0.30, 2.19 ± 0.32, 5.07 ± 0.23 and 1.78 ± 0.18 μM, respectively, and compounds 4–7 showed weak antibacterial activities against four bacterials respectively

Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NPhard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a &ldquo;spurious&rdquo; heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in &ldquo;spurious&rdquo; heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.<br /