Miracle-dz/UniFAR-FTUs huggingface dataset:
SepNet: A Deep Separation Matrix Prediction Network for Multichannel Audio Source Separation

In this paper, we propose SepNet, a deep neural network (DNN) designed to predict separation matrices from multichannel observations. One well-known approach to blind source separation (BSS) involves independent component analysis (ICA). A recently developed method called independent low-rank matrix analysis (ILRMA) is one of its powerful variants. These methods allow the estimation of separation matrices based on deterministic iterative algorithms. Specifically, ILRMA is designed to update the separation matrix according to an update rule derived based on the majorization-minimization principle. Although ILRMA performs reasonably well under some conditions, there is still room for improvement in terms of both separation accuracy and computation time, especially for large-scale microphone arrays. The existence of a deterministic iterative algorithm that can find one of the stationary points of the BSS problem implies that a DNN can also play that role if designed and trained properly. Motivated by this, we propose introducing a DNN that learns to convert a predefined input (e.g., an identity matrix) into a true separation matrix in accordance with a multichannel observation. To enable it to find one of the multiple solutions corresponding to different permutations of the source indices, we further propose adopting a permutation invariant training strategy to train the network. By using a fully convolutional architecture, we can design the network so that the forward propagation can be computed efficiently. The experimental results revealed that SepNet was able to find separation matrices faster and with better separation accuracy than ILRMA for mixtures of two sources.
Length No Longer Matters: A Real Length Adaptive Arrhythmia Classification Model with Multi-Scale Convolution

Although lots of arrhythmia classification models based on deep neural networks have been proposed, most of them can only be directly applied to inputs of a fixed length, which means the raw ECG records need to be padded or truncated before being put into the model. However, this process brings two main drawbacks: truncation may lead to information loss while padding increases calculation load. Besides, different sampling rates in other datasets may add trouble to transfer learning in this case. To address these problems, we propose a length adaptive arrhythmia classification model that can take advantage of raw ECG records of variable length. To further improve the overall performance, this model is designed with multi-scale convolution networks. We then introduce the dilated convolution that enables small convolution kernels to replace bigger traditional ones so as to enlarge feature’s reception fields and reduce training parameters. Finally, an attention mechanism is implemented so that the model can get efficiently trained and output enhanced results. Extensive experiments on the benchmark MIT-BIH database prove that our method is competitive with other state-of-the-arts.
ICASSP 2021 Acoustic Echo Cancellation Challenge: Integrated Adaptive Echo Cancellation with Time Alignment and Deep Learning-Based Residual Echo Plus Noise Suppression

This paper describes a three-stage acoustic echo cancellation (AEC) and suppression framework for the ICASSP 2021 AEC Challenge. In the first stage, a partitioned block frequency domain adaptive filtering is implemented to cancel the linear echo components without introducing the near-end speech distortion, where we compensate the time delay between the far-end reference signal and the micro-phone signal beforehand. In the second stage, a deep complex U-Net integrated with gated recurrent unit is proposed to further suppress the residual echo components. In the last stage, an extremely tiny deep complex U-Net is trained to suppress non-speech residual components that have not been suppressed completely in the second stage, which can also further increase the echo return loss enhancement (ERLE) without increasing the computational complexity dramatically. Experimental results show that the proposed three-stage framework can get the ERLE higher than 50 dB in both single-talk and double-talk scenarios, and perceptual evaluation of speech quality can be improved about 0.75 in double-talk scenarios. The proposed framework outperforms the AEC-Challenge baseline ResRNN by 0.12 points in terms of the MOS.
Kernel Learning with Tensor Networks

The expressive power of Gaussian Processes (GPs) is largely attributed to their kernel function, which highlights the crucial role of kernel design. Efforts in this direction include modern Neural Network (NN) based kernel design, which despite success, suffers from the lack of interpretability and tendency to overfit. To this end, we introduce a Tensor Network (TN) approach to learning kernel embeddings, with a TN serving to map the input to a low dimensional manifold, where a suitable base kernel function can be applied. The proposed framework allows for joint learning of the TN and base kernel parameters using stochastic variational inference, while leveraging on the low-rank regularization and multi-linear nature of TNs to boost model performance and provide enhanced interpretability. Performance evaluation within the regression paradigm against TNs and Deep Kernels demonstrates the potential of the framework, providing conclusive evidence for promising future extensions to other learning paradigms.
DP-VTON: Toward Detail-Preserving Image-Based Virtual Try-on Network

Image-based virtual try-on systems with the goal of transferring a target clothing item onto the corresponding region of a person have received great attention recently. However, it is still a challenge for the existing methods to generate photo-realistic try-on images while preserving non-target details(Fig. 1). To resolve this issue, we present a novel virtual try-on network, DP-VTON. First, a clothing warping module combines pixel transformation with feature transformation to transform the target clothing. Second, a semantic segmentation prediction module predicts a semantic segmentation map of the person wearing the target clothing. Third, an arm generation module generates arms of the reference image that will be changed after try-on. Finally, the warped clothing, semantic segmentation map, arms image and other non-target details (e.g. face, hair, bottom clothes) are fused together for try-on image synthesis. Extensive experiments demonstrate our system achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.1
Detecting Signal Corruptions in Voice Recordings For Speech Therapy

In this article we design an experimental setup to detect disturbances in voice recordings, such as additive noise, clipping, infrasound and random muting. The datasets are generated by introducing degradations into clean recordings. We test five different classification algorithms in both single- and multi-label settings: kernel substitution based support vector machine, convolutional neural network, long short-term memory (LSTM), and a hidden Markov model using either Gaussian mixture models or generative models in its state distribution. The LSTM achieved good results in both tests, most notably in the multi-label case where the average balanced accuracy was 82.7% on one dataset.
Empirically Accelerating Scaled Gradient Projection Using Deep Neural Network for Inverse Problems in Image Processing

Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms. One approach is to unfold finite number of iterations of conventional optimization algorithms and to learn parameters in the algorithms. However, these are forward methods and are indeed neither iterative nor convergent. Here, we present a novel DNN-based convergent iterative algorithm that accelerates conventional optimization algorithms. We train a DNN to yield parameters in scaled gradient projection method. So far, these parameters have been chosen heuristically, but have shown to be crucial for good empirical performance. In simulation results, the proposed method significantly improves the empirical convergence rate over conventional optimization methods for various large-scale inverse problems in image processing.
DHCN: Deep Hierarchical Context Networks For Image Annotation

Context modeling is one of the most fertile sub-fields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic relationships. However, the potential of context modeling is currently under-explored and most of the existing solutions are either context-free or restricted to simple handcrafted geometric relationships.We introduce in this paper DHCN: a novel Deep Hierarchical Context Network that leverages different sources of contexts including geometric and semantic relationships. The proposed method is based on the minimization of an objective function mixing a fidelity term, a context criterion and a regularizer. The solution of this objective function defines the architecture of a bi-level hierarchical context network; the first level of this network captures scene geometry while the second one corresponds to semantic relationships. We solve this representation learning problem by training its underlying deep network whose parameters correspond to the most influencing bi-level contextual relationships and we evaluate its performances on image annotation using the challenging ImageCLEF benchmark.
HebbNet: A Simplified Hebbian Learning Framework to do Biologically Plausible Learning

Backpropagation has revolutionized neural network training however, its biological plausibility remains questionable. Hebbian learning, a completely unsupervised and feedback free learning technique is a strong contender for a biologically plausible alternative. However, so far, it has neither achieved high accuracy performance vs. backprop, nor is the training procedure simple. In this work, we introduce a new Hebbian learning based neural network, called HebbNet. At the heart of HebbNet is an improved Hebbian approach that includes an updated activation threshold and gradient sparsity to the first principles of Hebbian learning. These enable an efficiently performing Hebbian approach with a simple training procedure. Further to this, the improved Hebbian rule also improves training dynamics by reducing the number of training epochs from 1500 to 200 and making training a one-step process from a two-step process. We also reduce heuristics by reducing hyper-parameters from 5 to 1, and number of search runs for hyper-parameter tuning from 12,600 to 13. Notwithstanding this, HebbNet still achieves strong test performance on MNIST and CIFAR-10 datasets vs. state-of-the-art.
Statistical Correction of Transcribed Melody Notes Based on Probabilistic Integration of a Music Language Model and a Transcription Error Model

This paper describes a statistical post-processing method for automatic singing transcription that corrects pitch and rhythm errors included in a transcribed note sequence. Although the performance of frame-level pitch estimation has been improved drastically by deep learning techniques, note-level transcription of singing voice is still an open problem. Inspired by the standard framework of statistical machine translation, we formulate a hierarchical generative model of a transcribed note sequence that consists of a music language model describing the pitch and onset transitions of a true note sequence and a transcription error model describing the addition of deletion, insertion, and substitution errors to the true sequence. Because the length of the true sequence might be different from that of the observed transcribed sequence, the most likely sequences with possible different lengths are estimated with Viterbi decoding and the most likely length is then selected with a sophisticated language model based on a long short-term memory (LSTM) network. The experimental results show that the proposed method can correct musically unnatural transcription errors.
Robust Deep Reinforcement Learning for Underwater Navigation with Unknown Disturbances

We study an underwater navigation problem, where an Under-water Autonomous Vehicle must reach a target position in the presence of a disturbance that may be unknown. In order to deal with this problem, we make use of Deep Reinforcement Learning tools, and more concretely, we make use of robust control ideas, which allow training an agent in the presence of uncertainty. We propose a robust Proximal Policy Optimization agent and train it using simulations of an underwater medium: this agent shows an excellent performance when facing unknown disturbances, being able to approach the performance of the optimal agent which had an exact knowledge of the underwater disturbance.
Language-Sensitive Music Emotion Recognition Models: are We Really There Yet?

Our previous research showed promising results when transferring features learned from speech to train emotion recognition models for music. In this context, we implemented a denoising autoencoder as a pretraining approach to extract features from speech in two languages (English and Mandarin). From that, we performed transfer and multi-task learning to predict classes from the arousal-valence space of music emotion. We tested and analyzed intra-linguistic and cross-linguistic settings, depending on the language of speech and lyrics of the music. This paper presents additional investigation on our approach, which reveals that: (1) performing pretraining with speech in a mixture of languages yields similar results than for specific languages - the pretraining phase appears not to exploit particular language features, (2) the music in Mandarin dataset consistently results in poor classification performance - we found low agreement in annotations, and (3) novel methodologies for representation learning (Contrastive Predictive Coding) may exploit features from both languages (i.e., pretraining on a mixture of languages) and improve classification of music emotions in both languages. From this study we conclude that more research is still needed to understand what is actually being transferred in these type of contexts.
Speech Enhancement Autoencoder with Hierarchical Latent Structure

A new hierarchical convolutional neural network-based autoencoder architecture called SEHAE (Speech Enhancement Hierarchical AutoEncoder) is introduced, in which the latent representation is decomposed into several parts that correspond to different scales. The model consists of three functionally different components. First, a stack of encoders generates a set of latent vectors that contain information from an increasingly larger receptive field. Second, the decoders construct the clean speech in a stage-wise and additive fashion, starting from a learned initial vector. The third component, which we call funnel networks, is tasked with "knitting" together the outputs of the previous decoder and the encoder to compute latent vectors for the next decoder. Several options for initial vectors are explored. Experiments show that SEHAE achieves significant improvements for the considered speech quality and intelligibility measures, outperforming a denoising autoencoder and other step-wise models. Furthermore, its internal workings are investigated using the intermediate results from the decoders.
Training Neural Networks with Domain Pattern-Aware Auxiliary Task for Sleep Staging

Recent studies on deep learning for biomedical data analysis have shown that guiding neural networks (NNs) to incorporate appropriate domain knowledge can enhance model performance. Accordingly, we present an auxiliary classification task for sleep staging to enable NNs to exploit clinically significant EEG patterns in data. Specifically, representations constructed from a global max pooling operator, promoting the NNs to extract distinctive signal patterns for each sleep stage, are adopted herein. While previous studies required additional training processes or parameter tuning phases for correct formulation of the domain knowledge during training, our method operates in an end-to-end manner without further calibration. During the experiments, the proposed method improved the classification accuracies compared to the original model. Moreover, intermediate representations in the proposed method were highly interpretable, wherein medically-significant features were correctly addressed with the activation values. Overall, the proposed method successfully augmented model qualities in two important aspects for adoption of AI in medical tasks: good model performance and clinically sound interpretation of models.
Effect of reducing ultraprocessed food consumption on obesity among US children and adolescents aged 7–18 years: evidence from a simulation model

Background Children and adolescents in the USA consume large amounts of daily calories from ultraprocessed foods (UPFs). Recent evidence links UPF consumption to increased body fat in youth. We aimed to estimate the potential impact of reducing UPF consumption on childhood obesity rate in the USA. Methods We developed a microsimulation model to project the effect of reducing UPF consumption in children’s diet on reducing the prevalence of overweight or obesity among US youth. The model incorporated nationally representative data on body mass index (BMI) percentile and dietary intake of 5804 children and adolescents aged 7–18 years from the National Health and Nutrition Examination Survey 2011–2016, and the effect of reducing UPF consumption on calorie intake from a recent randomised controlled trial. Uncertainties of model inputs were incorporated using probabilistic sensitivity analysis with 1000 simulations. Results Reducing UPFs in children’s diet was estimated to result in a median of −2.09 kg/m2 (95% uncertainty interval −3.21 to –0.80) reduction in BMI among children and adolescents aged 7–18 years. The median prevalence of overweight (BMI percentile ≥85th) and obesity (BMI percentile ≥95th percentile) was reduced from 37.0% (35.9%, 38.1%) to 20.9% (15.1%, 29.9%) and from 20.1% (19.2%, 21.0%) to 11.0% (7.86%, 15.8%), respectively. Larger BMI and weight reductions were seen among boys than girls, adolescents than children, non-Hispanic black and Hispanic youth than non-Hispanic white youth, and those with lower levels of parental education and family income. Conclusions Reducing UPF consumption in children’s diet has the potential to substantially reduce childhood obesity rate among children and adolescents in the USA.
Estimation to the turbulent kinetic energy dissipation rate and bottom shear stress in the tidal bottom boundary layer of the Yellow Sea

Abstract High frequency velocity fluctuations were measured by using an acoustic Doppler velocimeter (ADV) in the tidal bottom boundary layer (BBL) of the Yellow Sea (YS) for 25 h. The turbulent kinetic energy dissipation rate and the bottom shear stress were estimated and analyzed. Results show that: (1) in the tidal BBL of the YS the variations of the dissipation rate and the bottom shear stress during 25 h are (1.8 × 10−8−3.4× 10−5) W'kg−1 and 6.6 × 10−4−7.5 × 10 −1) N' m−2 respectively, indicating that there are strong dissipations in the tidal BBL of the YS (2) in the well-mixed tidal BBL, the turbulence is mainly shear-induced locally and the production and dissipation are generally in equilibrium (3) for the seas where the semidiurnal tidal current is dominant, both the dissipation rate and the bottom shear stress exhibit a strong quarter-diurnal variation (4) the mean bottom drag coefficient C¯ (0.45) is 0.0017 (corresponding C¯d(1.00 = 0.0015), but it has significant variations (0.00050−0082). Th.
S002531542100031Xjra 309..318

The present study reports polychaetes that bore into limestone rocks along the east coast of the Aegean Sea (eastern Mediterranean). Rock materials were collected at two depth intervals (0–5 and 5–10 m) at 15 stations in four localities of Ildırı Bay. A total of 276 specimens belonging to 12 species and four families (Eunicidae, Spionidae, Cirratulidae and Sabellidae) were recorded. Specimens belonging to Dodecaceria and Pseudopotamilla were identified at the genus level, because they differ from described species, were few in number or were in poor condition. Dipolydora giardia is a new species to the marine fauna of Turkey. The most dominant and frequent family in the area was Eunicidae, followed by Spionidae. Lysidice ninetta and L. margaritacea comprised 59% of the total number of individuals. The number of species and individuals, and the diversity index did not change with regard to depth or locality. Two species assemblages were found in the area, mainly formed by Dipolydora and Lysidice species. The Lessepsian species, Palola valida, which is a new record for the Aegean Sea, occurred abundantly at the study sites, posing a risk of damage to limestone rocks in the Mediterranean Sea. The morphological features of the species identified at the generic level and the burrow structure of these species are presented. The burrow shapes of Palola siciliensis and P. valida were described for the first time in the present study; they constructed complicated galleries, including more than four entrances.
A Mechanically Transmitted DNA Mycovirus Is Targeted by the Defence Machinery of Its Host, Botrytis cinerea

Eukaryotic circular single-stranded DNA (ssDNA) viruses were known only to infect plants and vertebrates until the discovery of the isolated DNA mycovirus from the fungus Sclerotinia sclerotiorum. Similar viral sequences were reported from several other sources and classified in ten genera within the Genomoviridae family. The current study reports two circular ssDNA mycoviruses isolated from the phytopathogen Botrytis cinerea, and their assignment to a newly created genus tentatively named Gemydayirivirus. The mycoviruses, tentatively named botrytis gemydayirivirus 1 (BGDaV1) and BGDaV2, are 1701 and 1693 nt long and encode three and two open reading frames (ORFs), respectively. Of the predicted ORFs, only ORF I, which codes for a replication initiation protein (Rep), shared identity with other proteins in GenBank. BGDaV1 is infective as cell-free purified particles and confers hypovirulence on its natural host. Investigation revealed that BGDaV1 is a target for RNA silencing and genomic DNA methylation, keeping the virus at very low titre. The discovery of BGDaV1 expands our knowledge of the diversity of genomoviruses and their interaction with fungal hosts.