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

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

Selective Conversion of Renewable Furfural with Ethanol to Produce Furan-2-acrolein Mediated by Pt@MOF‑5

The selective conversion of furfural (FUR) with ethanol to produce furan-2-acrolein has been successfully performed in the presence of O₂, in which metal organic framework (MOF) supported platinum nanoparticles (Pt@MOF-5, Pt@UIO-66, and Pt@UIO-66-NH₂) were used as the catalysts. Under the optimal conditions, 84.1% conversion of FUR and 90.1% selectivity of furan-2-acrolein was obtained with Pt@MOF-5 as the catalyst. Moreover, the catalysts were characterized by XRD, H₂-TPR, HRTEM, HRSEM, and XPS techniques, with the synergetic effect of well-dispersed platinum nanoparticles in the MOF-5 channel. In addition, the results of the recycling experiment exhibited that there was no significant loss after the catalyst was reused 5 times

A reverse dot blot assay for the screening of twenty mutations in four genes associated with NSHL in a Chinese population

<div><p>Background</p><p>Congenital deafness is one of the most distressing disorders affecting humanity and exhibits a high incidence worldwide. Most cases of congenital deafness in the Chinese population are caused by defects in a limited number of genes. A convenient and reliable method for detecting common deafness-related gene mutations in the Chinese population is required.</p><p>Methods</p><p>We developed a PCR-reverse dot blot (RDB) assay for screening 20 hotspot mutations of <i>GJB2</i>, <i>GJB3</i>, <i>SLC26A4</i>, and <i>MT-RNR1</i>, which are common non-syndromic hearing loss (NSHL)–associated genes in the Chinese population. The PCR-RDB assay consists of multiplex PCR amplifications of 10 fragments in the target sequence of the four above-mentioned genes in wild-type and mutant genomic DNA samples followed by hybridization to a test strip containing allele-specific oligonucleotide probes. We applied our method to a set of 225 neonates with deafness gene mutations and 30 normal neonates.</p><p>Results</p><p>The test was validated through direct sequencing in a blinded study with 100% concordance.</p><p>Conclusions</p><p>The results demonstrated that our reverse dot blot assay is a reliable and effective genetic screening method for identifying carriers and individuals with NSHL among the Chinese population.</p></div

NSHL gene mutation detection probes.

<p>NSHL gene mutation detection probes.</p

Multiplex PCR primers.

<p>Multiplex PCR primers.</p

Sustainable and Cost-Effective Protocol for Cascade Oxidative Condensation of Furfural with Aliphatic Alcohols

The oxidative condensation of furfural with aliphatic alcohols catalyzed by the supported Co_<i>x</i>O_<i>y</i>–N catalysts is developed in the presence of molecular oxygen. For the oxidative condensation process of furfural, <i>n</i>-propanol and dioxygen, a 75.1% conversion of furfural and 92.8% selectivity of 3-(furan-2-yl-)-2-methylacryaldehyde was obtained when the Co_<i>x</i>O_<i>y</i>–N@K-10 and cesium carbonate was employed as catalytic system. Moreover, the reaction conditions were optimized and the oxidative condensation of furfural with different aliphatic alcohols was also investigated. The synergistic effect between the Co_<i>x</i>O_<i>y</i>–N@K-10 and basic additive was considered to be responsible for this cascade process. Furthermore, a possible reaction mechanism is proposed for oxidative condensation of furfural–<i>n</i>-propanol–O₂ (FPO) system

PCR products of eight samples obtained by electrophoresis in a 1.5% agarose gel stained with ethidium bromide.

<p>I and II represents PCR products of tube I and II respectively.</p