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

Machine Learning Based PCB/Package Stack-up Optimization For Signal Integrity

PCB/package stack-up design optimization is time-consuming and requiring a great deal of experience. Although some iterative optimization algorithms are applied to implement automatic stack-up design, evaluating the results of each iteration is still time-intensive. This paper proposes a combined Bayesian optimization-artificial neural network (BO-ANN) algorithm, utilizing a trained ANN-based surrogate model to replace a 2D cross-section analysis tool for fast PCB/package stack-up design optimization. With the acceleration of ANN, the proposed BO-ANN algorithm can finish 100 iterations in 40 seconds while achieving the target characteristic impedance. To better generalize the BO-ANN algorithm, a strategy of effective dielectric calculation is applied to multiple-dielectric stack-up optimization. the BO-ANN algorithm will be able to output optimized stack-up designs with dielectric layers chosen from the pre-defined library and the obtained designs are verified by 2D solver

Near Field Scanning-Based EMI Radiation Root Cause Analysis in an SSD

In Modern Portable Electronic Devices, Solid-State Drives (SSDs) Are Commonly Used and Have Been Identified as One of the Dominant Electromagnetic Interference (EMI) Noise Sources that Can Cause RF Desensitization Issues. in This Paper, the EM Emission Source from an SSD Module is Identified and Analyzed using Near Field Scanning and Dipole Moment Source Reconstruction. the Identified Noise Current Path Including the Power Management Integrated Circuit and the Decoupling Capacitor is Validated with the Assistance of Full-Wave Simulation. the Measured Noise Voltage is Used as an Excitation in the Simulation and the Simulated Near Fields Showed a Good Correlation with Measured Near Fields in Both Pattern and Magnitude. based on the Validated Radiation Mechanism, an Optimized Layout is Proposed and Validated in Simulation Reducing the Far Field Radiation by 10 DB

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

Mendelian randomization to evaluate the causal relationship between liver enzymes and the risk of six specific bone and joint-related diseases

BackgroundStudies of liver dysfunction in relation to bone and joint-related diseases are scarce, and its causality remains unclear. Our objective was to investigate whether serum liver enzymes are causally associated with bone and joint-related diseases using Mendelian randomization (MR) designs.MethodsGenetic data on serum liver enzymes (alkaline phosphatase (ALP); alanine transaminase (ALT); gamma-glutamyl transferase (GGT)) and six common bone and joint-related diseases (rheumatoid arthritis (RA), osteoporosis, osteoarthritis (OA), ankylosing spondylitis, psoriatic arthritis, and gout) were derived from independent genome-wide association studies of European ancestry. The inverse variance-weighted (IVW) method was applied for the main causal estimate. Complementary sensitivity analyses and reverse causal analyses were utilized to confirm the robustness of the results.ResultsUsing the IVW method, the positive causality between ALP and the risk of osteoporosis diagnosed by bone mineral density (BMD) at different sites was indicated (femoral neck, lumbar spine, and total body BMD, odds ratio (OR) [95% CI], 0.40 [0.23–0.69], 0.35 [0.19–0.67], and 0.33 [0.22–0.51], respectively). ALP was also linked to a higher risk of RA (OR [95% CI], 6.26 [1.69–23.51]). Evidence of potential harmful effects of higher levels of ALT on the risk of hip and knee OA was acquired (OR [95% CI], 2.48 [1.39–4.41] and 3.07 [1.49–6.30], respectively). No causal relationship was observed between GGT and these bone and joint-related diseases. The study also found that BMD were all negatively linked to ALP levels (OR [95% CI] for TBMD, FN-BMD, and LS-BMD: 0.993 [0.991–0.995], 0.993 [0.988–0.998], and 0.993 [0.989, 0.998], respectively) in the reverse causal analysis. The results were replicated via sensitivity analysis in the validation process.ConclusionsOur study revealed a significant association between liver function and bone and joint-related diseases

Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches

Object detection has found extensive applications in various tasks, but it is also susceptible to adversarial patch attacks. Existing defense methods often necessitate modifications to the target model or result in unacceptable time overhead. In this paper, we adopt a counterattack approach, following the principle of "fight fire with fire," and propose a novel and general methodology for defending adversarial attacks. We utilize an active defense strategy by injecting two types of defensive patches, canary and woodpecker, into the input to proactively probe or weaken potential adversarial patches without altering the target model. Moreover, inspired by randomization techniques employed in software security, we employ randomized canary and woodpecker injection patterns to defend against defense-aware attacks. The effectiveness and practicality of the proposed method are demonstrated through comprehensive experiments. The results illustrate that canary and woodpecker achieve high performance, even when confronted with unknown attack methods, while incurring limited time overhead. Furthermore, our method also exhibits sufficient robustness against defense-aware attacks, as evidenced by adaptive attack experiments