Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Interference with Phase Recovery in DNN-based Monaural Singing Voice Separation

State-of-the-art methods for monaural singing voice separation consist in estimating the magnitude spectrum of the voice in the short-term Fourier transform (STFT) domain by means of deep neural networks (DNNs). The resulting magnitude estimate is then combined with the mixture's phase to retrieve the complex-valued STFT of the voice, which is further synthesized into a time-domain signal. However, when the sources overlap in time and frequency, the STFT phase of the voice differs from the mixture's phase, which results in interference and artifacts in the estimated signals. In this paper, we investigate on recent phase recovery algorithms that tackle this issue and can further enhance the separation quality. These algorithms exploit phase constraints that originate from a sinusoidal model or from consistency , a property that is a direct consequence of the STFT redundancy. Experiments conducted on real music songs show that those algorithms are efficient for reducing interference in the estimated voice compared to the baseline approach.
AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors

Abstract The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.
Front Seat Child Occupancy Detection Using Road Surveillance Camera Images

Camera based traffic enforcement solutions has been ubiquitously employed in many roadways around the world. Toll violation detection, red light violation detection, speed violation detection are typically the most common usages of camera systems towards traffic enforcement. However, there are other enforcement areas that need attention such as front seat child occupancy violation, which is a major safety concern in many countries. In this study, we propose a novel automated technique towards child occupancy detection in front seat of vehicles using deep learning algorithms. Using a camera system placed on an overhead gantry, installed on a high way in Turkey, real world images are captured during day and night time. We performed experiments using 1000 real world images and achieved an overall accuracy of 92%.
The Trajectory of Voice Onset Time with Vocal Aging

Vocal aging, a universal process of human aging, can largely affect one's language use, possibly including some subtle acoustic features of one's utterances like Voice Onset Time. To figure out the time effects, Queen Elizabeth's Christmas speeches are documented and analyzed in the long-term trend. We build statistical models of time dependence in Voice Onset Time, controlling a wide range of other fixed factors, to present annual variations and the simulated trajectory. It is revealed that the variation range of Voice Onset Time has been narrowing over fifty years with a slight reduction in the mean value, which, possibly, is an effect of diminishing exertion, resulting from subdued muscle contraction, transcending other non-linguistic factors in forming Voice Onset Time patterns over a long time.
Joint Discriminative Embedding Learning, Speech Activity and Overlap Detection for the DIHARD Speaker Diarization Challenge

The DIHARD is a new, annual speaker diarization challenge focusing on “hard” domains, i.e. datasets in which current state-of-the-art systems are expected to perform poorly. We present our diarization system, which is a neural network jointly optimized for speaker embedding learning, speech activity and overlap detection. We present our network topology and the afﬁnity matrix loss objective function responsible for learning the frame-wise speaker embeddings. The outputs of the network are then clustered with KMeans, and each frame classi-ﬁed with speech activity is assigned to one or two speakers, depending on the overlap detection. For the training data, we used two well-know meeting corpora - the AMI and the ICSI datasets, together with the provided samples from the DIHARD challenge. To further enhance our system, we present three data augmentation settings: the ﬁrst is a naive concatenation of isolated speaker utterances from non-diarization datasets, which generates artiﬁcial diarization prompts. The second is a simple noise addition with sampled signal-to-noise ratios. The third is using noise suppression over the development data. All training setups are compared in terms of diarization error rate and mutual information in the evaluation set of the challenge.
Host Dispersal Responses to Resource Supplementation Determine Pathogen Spread in Wildlife Metapopulations

Many wildlife species occupy landscapes that vary in the distribution, abundance, and quality of food resources. Increasingly, urbanized and agricultural habitats provide supplemental food resources that can have profound consequences for host distributions, movement patterns, and pathogen exposure. Understanding how host and pathogen dispersal across landscapes is affected by the spatial extent of food-supplemented habitats is therefore important for predicting the consequences for pathogen spread and impacts on host occupancy. Here we develop a generalizable metapopulation model to understand how the relative abundance of provisioned habitats across the landscape and how the host dispersal responses to provisioning and infection influence patch occupancy by hosts and their pathogens. We find that pathogen invasion and landscape-level infection prevalence are greatest when provisioning increases patch attractiveness and disperser production and when infection has minimal costs on dispersal success. Alternatively, if provisioning promotes site fidelity or reduces disperser production, increasing the fraction of food-supplemented habitats can reduce landscape-scale infection prevalence and minimize disease-induced declines in host occupancy. This work highlights the importance of considering how resources and infection jointly influence host dispersal for predicting how changing resource distributions influence the spread of infectious diseases.
A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification

One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.
Deep Blind Video Quality Assessment Based on Temporal Human Perception

The high performance video quality assessment (VQA) algorithm is a necessary skill to provide high quality video to viewers. However, since the nonlinear perception function between the distortion level of the video and the subjective quality score is not precisely defined, there are many limitations in accurately predicting the quality of the video. In this paper, we propose a deep learning scheme named Deep Blind Video Quality Assessment (DeepBVQA) to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before. We used CNN to extract the spatial cues of each video in VQA and proposed new hand-crafted features for temporal cues. Performance experiments show that performance is better than other state-of-the-art no-reference (NR) VQA models and the introduction of hand-crafted temporal features is very efficient in VQA.
Female Life-History Trade-Offs and the Maintenance of Genetic Variation in Drosophila melanogaster

Why do we observe substantial variation in fitness-related traits under strong natural or sexual selection? While there is support for several selective and neutral mechanisms acting in select systems, we lack a comprehensive analysis of the relative importance of various mechanisms within a single system. Furthermore, while sexually selected male traits have been a central focus of this paradox, female sexual traits have rarely been considered. In this study, I evaluate the contribution of various selective mechanisms to the maintenance of substantial variation in female attractiveness and offspring production observed among Drosophila melanogaster genotypes. I tested for contributions from antagonistic pleiotropy, frequency-dependent selection, changing environments, and sexual conflict. I found negative genetic correlations between some traits (male attractiveness vs. female resistance to male harm, early-life offspring production vs. reproductive senescence) and genotype-specific changes in fitness between environments. However, no measurement found strong trade-offs among the fitness components of these genotypes. Overall, I find little evidence that any one mechanism is strong enough to maintain genetic variation on its own. Instead, I suggest that many mechanisms may weaken the selection among genotypes, which would collectively allow neutral processes such as mutation-selection balance to maintain genetic variation within populations.
Feature with Complementarity of Statistics and Principal Information for Spoofing Detection

Constant-Q transform (CQT) has demonstrated its effectiveness in anti-spooﬁng feature analysis for automatic speaker veriﬁ-cation. This paper introduces a statistics-plus-principal information feature where a short-term spectral statistics information (STSSI), octave-band principal information (OPI) and full-band principal information (FPI) are proposed on the basis of CQT. Firstly, in contrast to conventional utterance-level long-term statistic information, STSSI reveals the spectral statistics at frame-level, moreover it provides a feasibility condition for model training while only small training database is available. Secondly, OPI emphasizes the principal information for octave-bands, STSSI and OPI creates a strong complementarity to enhance the anti-spooﬁng feature. Thirdly, FPI is also of complementary effect with OPI. With the statistical property over CQT spectral domain and the principal information through discrete cosine transform (DCT), the proposed statistics-plus-principal feature shows reasonable advantage of the complementary trait for spooﬁng detection. In this paper, we setup deep neural network (DNN) classiﬁers for evaluation of the features. Experiments show the effectiveness of the proposed feature as compared to many conventional features on ASVspoof 2017 and ASVspoof 2015 corpus.
Conditional-Computation-Based Recurrent Neural Networks for Computationally Efficient Acoustic Modelling

The ﬁrst step in Automatic Speech Recognition (ASR) is a ﬁxed-rate segmentation of the acoustic signal into overlapping windows of ﬁxed length. Although this procedure allows to achieve excellent recognition accuracy, it is far from being computationally efﬁcient, in that it may produce a highly redundant signal (i.e, almost identical spectral vectors may span many observation windows) that converts into computational overload. The reduction of such overload can be very beneﬁcial for application such as ofﬂine ASR on mobile devices. In this paper we present a principled way for saving numerical operations during ASR by using conditional-computation methods in deep bidirectional Recurrent Neural Networks (RNNs) for acoustic modelling. The methods rely on learned binary neurons that allow hidden layers to be updated only when necessary or to keep their previous value. We (i) evaluate, for the ﬁrst time, conditional computation-based recurrent architectures on a speech recognition task, and (ii) propose a novel model speciﬁcally designed for speech data that inherently builds a multi-scale temporal structure in the hidden layers. Results on the TIMIT dataset show that conditional mechanisms in recurrent architectures can reduce hidden layer updates up to 40% at the cost of about 20% relative phone error rate increase.
A Prototype Towards Modeling Visual Data Using Decentralized Generative Adversarial Networks

Decentralized computation is crucial for training on large data sets, which are stored in various locations. This paper proposes a method for collaboratively training generative adversarial networks (GANs) on several nodes to approach this problem. Each node has access to its local private dataset and model, but can influence and can be influenced by neighboring nodes. Such an approach avoids the need of a fusion center, offers better network load balance and improves data privacy. This work proposes an initial approach for decentralized GAN network optimization which is based on discriminator exchange among neighbors using an information gain criterion. The initial experiments are presented to verify whether such a decentralized architecture can provide useful results.
Developmental phosphoproteomics identifies the kinase CK2 as a driver of Hedgehog signaling and a therapeutic target in medulloblastoma

Inhibitors of the kinase CK2 may halt the growth of an aggressive form of medulloblastoma. A targeted, resilient treatment for medulloblastoma Medulloblastoma is an aggressive type of brain tumor that most often arises in children and lacks targeted therapeutic options. The subtypes driven by activity in the sonic hedgehog (SHH) pathway are particularly resistant to current drugs, such as those known as SMO inhibitors, which target this pathway. Purzner et al. used phosphoproteomics to track the development of mouse cells that give rise to medulloblastoma and identified the kinase CK2 as a likely target. CK2 inhibitors blocked the growth of SMO inhibitor–resistant, SHH-type human and mouse medulloblastoma cells and markedly extended the survival of tumor-bearing mice, in which the drug was well tolerated. One of the compounds also blocked the growth of tumors that had mutant CK2, suggesting that it is less susceptible to a common mode of drug resistance. A clinical trial is under way to test this inhibitor in pediatric patients. A major limitation of targeted cancer therapy is the rapid emergence of drug resistance, which often arises through mutations at or downstream of the drug target or through intrinsic resistance of subpopulations of tumor cells. Medulloblastoma (MB), the most common pediatric brain tumor, is no exception, and MBs that are driven by sonic hedgehog (SHH) signaling are particularly aggressive and drug-resistant. To find new drug targets and therapeutics for MB that may be less susceptible to common resistance mechanisms, we used a developmental phosphoproteomics approach in murine granule neuron precursors (GNPs), the developmental cell of origin of MB. The protein kinase CK2 emerged as a driver of hundreds of phosphorylation events during the proliferative, MB-like stage of GNP growth, including the phosphorylation of three of the eight proteins commonly amplified in MB. CK2 was critical to the stabilization and activity of the transcription factor GLI2, a late downstream effector in SHH signaling. CK2 inhibitors decreased the viability of primary SHH-type MB patient cells in culture and blocked the growth of murine MB tumors that were resistant to currently available Hh inhibitors, thereby extending the survival of tumor-bearing mice. Because of structural interactions, one CK2 inhibitor (CX-4945) inhibited both wild-type and mutant CK2, indicating that this drug may avoid at least one common mode of acquired resistance. These findings suggest that CK2 inhibitors may be effective for treating patients with MB and show how phosphoproteomics may be used to gain insight into developmental biology and pathology.
Deep Residual Network for Enhancing Quality of the Decoded Intra Frames of Hevc

High Efficiency Video Coding (HEVC) has been widely used to encode video sequences and output streams for its outstanding performance on compression. However, the lossy compression process still results in blocking, blurring and ring effects, which are quite significant at low bit-rates. In this paper, we propose a post-processing method based on a residual convolutional neural network (CNN) to effectively improve the visual quality of the decoded intra frames of HEVC. By learning the spatial mapping between high and low quality image patches, our approach can effectively predict lost image details. Since the proposed approach only works on decoded frames, it does not require to modify the original HEVC, i.e., the enhancement can be done in parallel with HEVC baseline, which makes it flexible to be included into a decoding system. Experimental results show that the proposed approach outperforms the state-of-the-arts by a large margin and makes a 5.5% bit-rate reduction on average compared to HEVC baseline. Furthermore, the PSNR measures of intra frames increase about 0.395 dB when encoded at high quality parameters (QP).
Character Recognition in Images

— This paper tackles the problem of recognizing characters in images of natural scenes which is a challenging problem that has received significant amount of attention. Recently, many methods have been proposed to design better feature representatives and models for text detection and character recognition. In machine learning, we apply these recently developed large-scale algorithms for learning features automatically from unlabeled data and access the performance of various features based on these methods. Nearest neighbor and SVM classification allow us to construct highly effective classifiers for both detection and recognition, that are to be used in a high accuracy end-to-end system that can be far superior to that of commercial Optical Character Recognition systems.
NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.
WaveNet Vocoder with Limited Training Data for Voice Conversion

This paper investigates the approaches of building WaveNet vocoders with limited training data for voice conversion (VC). Current VC systems using statistical acoustic models always suffer from the quality degradation of converted speech. One of the major causes is the use of hand-crafted vocoders for wave-form generation. Recently, with the emergence of WaveNet for waveform modeling, speaker-dependent WaveNet vocoders have been proposed and they can reconstruct speech with better quality than conventional vocoders, such as STRAIGHT. Because training a WaveNet vocoder in the speaker-dependent way requires a relatively large training dataset, it remains a challenge to build a high-quality WaveNet vocoder for VC tasks when the training data of target speakers is limited. In this paper, we propose to build WaveNet vocoders by combining the initialization using a multi-speaker corpus and the adaptation using a small amount of target data, and evaluate this proposed method on the Voice Conversion Challenge (VCC) 2018 dataset which contains approximately 5 minute recordings for each target speaker. Experimental results show that the WaveNet vocoders built using our proposed method outperform conventional STRAIGHT vocoder. Furthermore, our system achieves an average naturalness MOS of 4.13 in VCC 2018, which is the highest among all submitted systems.
Image Filtering with Advectors

We propose to consider linear and nonlinear image diffusion processes as the result of the transport of image values in external advection fields. Linear diffusion is seen as the continuous transport of particles in fields of random orientations, while non-linear behavior is obtained by modulating the magnitude of these fields according to local edge strength. Edge sharpening can be achieved by biasing fields towards image edges using e.g. Gradient Vector Flow. This point of view, closely related to discrete random walks and other stochastic interpretations of diffusion processes, allows for simple, stable and surprisingly efficient filters that can be implemented using a few lines of code only.