Sample Mixed-Based Data Augmentation for Domestic Audio Tagging

Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip. Recently deep learning methods have been applied to audio tagging and have achieved state-of-the-art performance, which provides a poor generalization ability on new data. However due to the limited size of audio tagging data such as DCASE data, the trained models tend to result in overfitting of the network. Previous data augmentation methods such as pitch shifting, time stretching and adding background noise do not show much improvement in audio tagging. In this paper, we explore the sample mixed data augmentation for the domestic audio tagging task, including mixup, SamplePairing and extrapolation. We apply a convolutional recurrent neural network (CRNN) with attention module with log-scaled mel spectrum as a baseline system. In our experiments, we achieve an state-of-the-art of equal error rate (EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming the baseline system without data augmentation.Comment: submitted to the workshop of Detection and Classification of Acoustic Scenes and Events 2018 (DCASE 2018), 19-20 November 2018, Surrey, U

Idiopathic Ventricular Arrhythmias Originating From the Pulmonary Sinus Cusp Prevalence, Electrocardiographic/Electrophysiological Characteristics, and Catheter Ablation

AbstractBackgroundIdiopathic ventricular arrhythmias (VAs) originating from the pulmonary sinus cusp (PSC) have not been sufficiently clarified.ObjectivesThe goal of this study was to investigate the prevalence, electrocardiographic characteristics, mapping, and ablation of idiopathic VAs arising from the PSC.MethodsData were analyzed from 218 patients undergoing successful endocardial ablation of idiopathic VAs with a left bundle branch block morphology and inferior axis deviation.ResultsTwenty-four patients had VAs originating from the PSC. In the first 7 patients, initial ablation performed in the right ventricular outflow tract failed to abolish the clinical VAs but produced a small change in the QRS morphology in 3 patients. In all 24 patients, the earliest activation was eventually identified in the PSC, at which a sharp potential was observed preceding the QRS complex onset by 28.2 ± 2.9 ms. The successful ablation site was in the right cusp (RC) in 10 patients (42%), the left cusp (LC) in 8 (33%), and the anterior cusp (AC) in 6 (25%). Electrocardiographic analysis showed that RC-VAs had significantly larger R-wave amplitude in lead I and a smaller aVL/aVR ratio of Q-wave amplitude compared with AC-VAs and LC-VAs, respectively. The R-wave amplitude in inferior leads was smaller in VAs localized in the RC than in the LC but did not differ between VAs from the AC and LC.ConclusionsVAs arising from the PSC are not uncommon, and RC-VAs have unique electrocardiographic characteristics. These VAs can be successfully ablated within the PSC

Value Assessment of Health Losses Caused by PM2.5 in Changsha City, China

With the advancement of urbanization, the harm caused to human health by PM2.5 pollution has been receiving increasing attention worldwide. In order to increase public awareness and understanding of the damage caused by PM2.5 in the air and gain the attention of relevant management departments, Changsha City is used as the research object, and the environmental quality data and public health data of Changsha City from 2013 to 2017 are used. All-cause death, respiratory death, cardiovascular death, chronic bronchitis, and asthma were selected as the endpoints of PM2.5 pollution health effects, according to an exposure–response coefficient, Poisson regression model, and health-impact-assessment-related methods (the Human Capital Approach, the Willingness to Pay Approach, and the Cost of Illness Approach), assessing the health loss and economic loss associated with PM2.5. The results show that the pollution of PM2.5 in Changsha City is serious, which has resulted in extensive health hazards and economic losses to local residents. From 2013 to 2017, when annual average PM2.5 concentrations fell to 10 μg/m3, the total annual losses from the five health-effect endpoints were $2788.41 million,$2123.18 million, $1657.29 million,$1402.90 million, and $1419.92 million, respectively. The proportion of Gross Domestic Product (GDP) in the current year was 2.69%, 1.87%, 1.34%, 1.04% and 0.93%, respectively. Furthermore, when the concentration of PM2.5 in Changsha City drops to the safety threshold of 10 μg/m3, the number of affected populations and health economic losses can far exceed the situation when it falls to 35 μg/m3, as stipulated by the national secondary standard. From 2013 to 2017, the total loss under the former situation was 1.48 times, 1.54 times, 1.86 times, 2.25 times, and 2.33 times that of the latter, respectively. Among them, all-cause death and cardiovascular death are the main sources of health loss. Taking 2017 as an example, when the annual average concentration dropped to 10 μg/m3, the health loss caused by deaths from all-cause death and cardiovascular disease was 49.16% of the total loss and 35.73%, respectively. Additionally, deaths as a result of respiratory disease, asthma, and chronic bronchitis contributed to 7.31%, 7.29%, and 0.51% of the total loss, respectively. The research results can provide a reference for the formulation of air pollution control policies based on health effects, which is of great significance for controlling air pollution and protecting people’s health

Deep-Learning-Based Carrier Frequency Offset Estimation and Its Cross-Evaluation in Multiple-Channel Models

The most widely used Wi-Fi wireless communication system, which is based on OFDM, is currently developing quickly. The receiver must, however, accurately estimate the carrier frequency offset between the transmitter and the receiver due to the characteristics of the OFDM system that make it sensitive to carrier frequency offset. The autocorrelation of training symbols is typically used by the conventional algorithm to estimate the carrier frequency offset. Although this method is simple to use and low in complexity, it has poor estimation performance at low signal-to-noise ratios, which has a significant negative impact on the performance of the wireless communication system. Meanwhile, the design of the communication physical layer using deep-learning-based (DL-based) methods is receiving more and more attention but is rarely used in carrier frequency offset estimation. In this paper, we propose a DL-based carrier frequency offset (CFO) model architecture for 802.11n standard OFDM systems. With regard to multipath channel models with varied degrees of multipath fadding, the estimation error of the proposed model is 70.54% lower on average than that of the conventional method under 802.11n standard channel models, and the DL-based method can outperform the estimation range of conventional methods. Besides, the model trained in one channel environment and tested in another was cross-evaluated to determine which models could be used for deployment in the real world. The cross-evaluation demonstrates that the DL-based model can perform well over a large class of channels without extra training when trained under the worst-case (most severe) multipath channel model

Sample mixed-based data augmentation for domestic audio tagging

Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip. Recently deep learning methods have been applied to audio tagging and\ud have achieved state-of-the-art performance, which provides a poor generalization ability on new data. However due to the limited size of audio tagging data such as DCASE data, the trained models tend to result in overfitting of the network. Previous data augmentation methods such as pitch shifting, time stretching and adding background noise do not show much improvement in audio tagging. In this paper, we explore the sample mixed data augmentation for the domestic audio tagging task, including mixup, SamplePairing and extrapolation. We apply a convolutional recurrent neural network (CRNN) with attention module with log-scaled mel spectrum as a baseline system. In our experiments, we achieve an state-of-the-art of equal error rate (EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming the baseline system without data augmentation

A Family of Automatic Modulation Classification Models Based on Domain Knowledge for Various Platforms

Identifying the modulation type of radio signals is challenging in both military and civilian applications such as radio monitoring and spectrum allocation. This has become more difficult as the number of signal types increases and the channel environment becomes more complex. Deep learning-based automatic modulation classification (AMC) methods have recently achieved state-of-the-art performance with massive amounts of data. However, existing models struggle to achieve the required level of accuracy, guarantee real-time performance at edge devices, and achieve higher classification performance on high-performance computing platforms when deployed on various platforms. In this paper, we present a family of AMC models based on communication domain knowledge for various computing platforms. The higher-order statistical properties of signals, customized data augmentation methods, and narrowband convolution kernels are the domain knowledge that is specifically employed to the AMC task and neural network backbone. We used separable convolution and depth-wise convolution with very few residual connections to create our lightweight model, which has only 4.61k parameters while maintaining accuracy. On the four different platforms, the classification accuracy and inference time outperformed those of the existing lightweight models. Meanwhile, we use the squeeze-and-excitation attention mechanism, channel shuffle module, and expert feature parallel branch to improve the classification accuracy. On the three most frequently used benchmark datasets, the high-accuracy models achieved state-of-the-art average accuracies of 64.63%, 67.22%, and 65.03%, respectively. Furthermore, we propose a generic framework for evaluating the complexity of deep learning models and use it to comprehensively assess the complexity strengths of the proposed models