BACKGROUND: Whole genome bisulfite sequencing (WGBS), possesses the aptitude to dissect methylation status at the nucleotide-level resolution of 5-methylcytosine (5-mC) on a genome-wide scale. It is a powerful technique for epigenome in various cell types, and tissues. As a recently established next-generation sequencing (NGS) platform, GenoLab M is a promising alternative platform. However, its comprehensive evaluation for WGBS has not been reported. We sequenced two bisulfite-converted mammal DNA in this research using our GenoLab M and NovaSeq 6000, respectively. Then, we systematically compared those data via four widely used WGBS tools (BSMAP, Bismark, BatMeth2, BS-Seeker2) and a new bisulfite-seq tool (BSBolt). We interrogated their computational time, genome depth and coverage, and evaluated their percentage of methylated Cs. RESULT: Here, benchmarking a combination of pre- and post-processing methods, we found that trimming improved the performance of mapping efficiency in eight datasets. The data from two platforms uncovered ~ 80% of CpG sites genome-wide in the human cell line. Those data sequenced by GenoLab M achieved a far lower proportion of duplicates (~ 5.5%). Among pipelines, BSMAP provided an intriguing representation of 5-mC distribution at CpG sites with 5-mC levels > ~ 78% in datasets from human cell lines, especially in the GenoLab M. BSMAP performed more advantages in running time, uniquely mapped reads percentages, genomic coverage, and quantitative accuracy. Finally, compared with the previous methylation pattern of human cell line and mouse tissue, we confirmed that the data from GenoLab M performed similar consistency and accuracy in methylation levels of CpG sites with that from NovaSeq 6000. CONCLUSION: Together we confirmed that GenoLab M was a qualified NGS platform for WGBS with high performance. Our results showed that BSMAP was the suitable pipeline that allowed for WGBS studies on the GenoLab M platform.

Superpixel based label propagation models have been successfully used for Hyperspectral Images (HSIs) classification especially when the training data are limited. However, it is inevitable that there are segmentation errors leading to data in one superpixel containing samples from different classes which could decrease the classification accuracy. In order to address this issue, we propose Superpixel Correction based Label Propagation for HSIs classification. First, superpixel segmentation technique is adopted to segment a HSI into many superpixel blocks. Then, clustering model density peak is used to adaptively cluster the data in each superpixel block to correct the segmentation errors. Finally, based on the corrected superpixel segmentation we construct global-local and spatial-spectral similarity graphs which results into effective propagation matrix for label propagation. The proposed model is verified in two HSIs data sets, and the experimental results demonstrate that the proposed model is superior to several state-of-the-art methods in terms of classification accuracy, especially in the case of limited training samples.

Hyperspectral images (HSIs) consisting of abundant spectral bands could lead to the curse of dimensionality issue when performing HSIs classification. In this letter, an unsupervised dimensionality reduction (DR) method termed Laplacian regularized collaborative representation projection (LRCRP) is proposed, where Laplacian regularization and local enhancement are introduced into collaborative representation (CR) to construct adjacent graph and then to reduce the spectral dimension in graph embedding framework. As the constructed graph simultaneously preserves the local manifold and global information in HSIs, the proposed LRCRP could be used to extract effective low-dimensional features for accurate HSIs classification. The experimental results on two HSI datasets demonstrate the effectiveness of the proposed model. The source code the proposed model is available athttps://github.com/XinweiJiang/LRCRP.

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace clustering has been proven to be powerful for exploiting the intrinsic relationship between data points. Despite the impressive performance in the HSI clustering, traditional subspace clustering methods often ignore the inherent structural information among data. In this article, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called graph convolutional subspace clustering (GCSC) for robust HSI clustering. Specifically, the framework recasts the self-expressiveness property of the data into the non-Euclidean domain, which results in a more robust graph embedding dictionary. We show that traditional subspace clustering models are the special forms of our framework with the Euclidean data. On the basis of the framework, we further propose two novel subspace clustering models by using the Frobenius norm, namely efficient GCSC (EGCSC) and efficient kernel GCSC (EKGCSC). Each model has a globally optimal closed-form solution, making it easier to implement, train, and apply in practice. Extensive experiments strongly evidence that EGCSC and EKGCSC dramatically outperform current models on three popular HSI data sets consistently.

Whole-organism metabolic rate co-varies allometrically with body mass, and is also affected by temperature through different biochemical mechanisms. Here we implement a computational platform to map specific quantitative trait loci (QTLs) that govern the dependence of metabolic rate on size and temperature. The model was formulated within settings of genetic mapping or genome-wide association studies through a mapping population genotyped by a set of molecular markers throughout the genome and phenotyped for metabolic parameters over a range of temperature. The model, estimated by a maximum-likelihood approach, allows a genome-wide search for the underlying metabolic QTLs and the estimation of genotype-specific parameters that specify the metabolism of an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.

This paper was intend to reveal the changes and grasp the trend of vegetation coverage in Shijiazhuang from 1995 to 2015. Firstly, dimidiate pixel model was used to calculate 6 periods of fractional vegetation coverage in Shijiazhuang based on Landsat TM/OLI data of 1995, 2001, 2007, 2009, 2012 and 2015, then CV model and Slope model were used to analysis spatial change characteristics and the trend, finally CA-Markov model was used to predict the vegetation coverage condition in Shijiazhuang in 2018. Results showed that the average of fractional vegetation coverage increased 3.71% from 1995 to 2015; the average CV of the whole city was 0.211, the main reason for vegetation coverage changes is human intervention; almost invariant and three levels of getting better areas were 82.22% of the city, three levels of getting worse were distributed in urban construction and economy development active areas; by 2018, the areas of high and medium-high vegetation coverage in Shijiazhuang will decrease, the areas of medium and low vegetation coverage will increase, the areas of very-low vegetation coverage will be almost invariant, the changes of vegetation coverage in the whole Shijiazhuang will trend to be stable.

Music auto-tagging has been an active research topic as it learns the relationship between the content of audio tracks and semantic tags such that users can query by both tags and audio segments without being troubled by the cold start problem. In this paper, we propose a new trigger-based context model to refine the existing content model based auto-tagging systems. The trigger based context model improves accruacy of weakly labeled tags in “Genre”, “Solo” and “Usage” by 10.63%, 10% and 26.43% respectively, which are usually poorly modeled due to lack of data in the content model based systems. Experiment results indicate that a combination of the content and context models outperforms the content based only auto-tagging system and the baseline Turnbull's MixHier model by 0.74% and 2.64% in average precision rate respectively.

Knowledge about biological shape has important implications in biology and biomedicine, but the underlying genetic mechanisms for shape variation have not been well studied. Statistical models play a pivotal role in mapping specific quantitative trait loci (QTLs) that contribute to biological shape and its developmental trajectories. We describe and assess a statistical framework for shape gene identification that incorporates shape and image analysis into a mixture-model framework for QTL mapping. Statistical parameters that define genotype-specific differences in biological shape are estimated by implementing statistical and computational algorithms. A state-of-the-art procedure is described to examine the control patterns of specific QTLs on the origin, properties and functions of biological shape. The statistical framework described will help to address many integrative biological and genetic questions and challenges in shape variation faced by the life sciences community.

As a group of economically important species, linkage mapping of polysomic autotetraploids, including potato, sugarcane and rose, is difficult to conduct due to their unique meiotic property of double reduction that allows sister chromatids to enter into the same gamete. We describe and assess a statistical model for mapping quantitative trait loci (QTLs) in polysomic autotetraploids. The model incorporates double reduction, built in the mixture model-based framework and implemented with the expectation-maximization algorithm. It allows the simultaneous estimation of QTL positions, QTL effects and the degree of double reduction as well as the assessment of the estimation precision of these parameters. We performed computer simulation to examine the statistical properties of the method and validate its use through analyzing real data in tetraploid switchgrass.

Flood disaster is one of the natural disasters occurred with the highest frequency in china, especially in the middle reaches of Yangtze River. In this paper, the submerged area and dynamic changes of flooding area was monitored using 250m-resolution MODIS images from 2000-2010. Results indicate that the inundated area of the Poyang Lake in 2010 is larger than any other years. The inundated area rose up between 2000 and 2010 for the Poyang Lake, while the inundated area of the east Dongting Lake and the southwest Dongting Lake showed a clear downward trend. The longest duration of fluctuation is up to 5-6 months for the three lakes. Summer is the season with higher frequency of flooding. The submerged depth of three Lakes concentrated in 0-10m which accounts for more than 70%. During 2000-2010, the loss of land area gradually declined, however, the number of affected population has a upward trend.

In the problem of classification of audio signals, the requirements of low-complexity, high-accuracy and short delay are crucial for some practical scenarios. This paper proposes a method of real-time speech/music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected together with a proposed simple harmonic structure stability feature, which is based on a rough estimation of the harmonic structure. A feature subset selection tool is used to select a subset of short and long term features to feed into a hierarchical oblique decision tree classifier. The method is evaluated and compared with the open loop selection mode in AMR-WB+. Experiments show the proposed approach gives a better performance (98.3%) compared to other prevailing approaches. In particular, it comes with promising short delay of 10 ms and low complexity of 1 wmops.

Although technologies of both low-level and high-level descriptors for music information retrieval (MIR) are advancing, there are some essential deficiencies while utilizing them separately. In this paper we propose a model where the low-level and high-level descriptors collaborate to support semantics-based MIR. The ontology of ldquomusic scenerdquo domain is constructed as a demonstration, and a set of domain related low-level and high-level descriptor analyses are introduced. Given the domain ontology and the analysis results as input, an abduction process is adopted to compute the semantics-based interpretations. Evaluations show that the collaborative model does not only give a better recall rate of semantics-based retrieval than separated models, but also maintains a promising precision meanwhile.

In this paper, the probability distribution functions (pdf's) of the formant spaces of three major accents of the English language, namely, British Received Pronunciation (RP), General American, and Broad Australian, are modeled and compared. The statistical differences across the formant spaces of these accents are employed for accent conversion. An improved formant tracking method, based on linear prediction (LP) feature analysis and a two-dimensional hidden Markov model (2-D-HMM) of format trajectories, is used for estimation of the formant trajectories of vowels and diphthongs of each accent. Comparative analysis of the formant spaces of the three accents indicates that these accents are partly conveyed by the differences of the formants of vowels. The estimates of the probability distributions of the formants for each accent are used in a speech synthesis system for accent conversion. Accent synthesis, through modification of the acoustic parameters of speech, provides a means of assessing the perceptual contribution of each formant parameter on conveying an accent. The results of perceptual evaluations of accent conversion illustrate that formants play an important role in conveying accents

This paper presents a post-processing speech restoration module for enhancing the performance of conventional speech enhancement methods. The restoration module aims to retrieve parts of speech spectrum that may be lost to noise or suppressed when using conventional speech enhancement methods. The proposed restoration method utilizes a harmonic plus noise model (HNM) of speech to retrieve damaged speech structure. A modified HNM of speech is proposed where, instead of the conventional binary labeling of the signal in each subband as voiced or unvoiced, the concept of harmonicity is introduced which is more adaptable to the codebook mapping method used in the later stage of enhancement. To restore the lost or suppressed information, an HNM codebook mapping technique is proposed. The HNM codebook is trained on speaker-independent speech data. To reduce the sensitivity of the HNM codebook to speaker variability, a spectral energy normalization process is introduced. The proposed post-processing method is tested as an add-on module with several popular noise reduction methods. Evaluations of the performance gain obtained from the proposed post-processing are presented and compared to standard speech enhancement systems which show substantial improvement gains in perceptual quality

This paper presents a method for restoration of the missing bandwidth of narrowband speech signals. Speech is decomposed into a linear prediction (LP) model of the spectral envelop and a harmonic plus noise model (HNM) of speech excitation. The LP spectral envelope and HNM excitation parameters of the narrowband speech are extrapolated using codebooks trained on narrowband and wideband speech. A novel contribution of this paper is the introduction of a parametric measure of the harmonicity of excitation in harmonically-spaced sub-bands. The wideband LSF parameters and the degree of harmonicity of missing excitation are estimated from those of the narrowband speech via codebook mapping. The method is successful in restoring the harmonicity of speech and converts telephone quality speech to perceptually high quality wideband speech.

This paper presents a time-frequency estimator for enhancement of noisy speech in the DFT domain. The time-varying trajectories of the DFT of speech and noise in each channel are modeled by low order autoregressive processes incorporated in the state equation of Kalman filters. The parameters of the Kalman filters are estimated recursively from the signal and noise in DFT channels. The issue of convergence of the Kalman filters to noise statistics during the noise-dominated periods is addressed and a method is incorporated for restarting of Kalman filters after long periods of noise-dominated activity in each DFT channel. The performance of the proposed method is compared with cases where the noise trajectories are not explicitly modeled. Evaluations show that the proposed method results in substantial improvement in perceived quality of speech.

This paper compares and quantifies the differences between formants of speech across accents. The cross entropy information measure is used to compare the differences between the formants of the vowels of three major English accents namely British, American and Australian. An improved formant estimation method, based on a linear prediction (LP) model feature analysis and a hidden Markov model (HMM) of formants, is employed for estimation of formant trajectories of vowels and diphthongs. Comparative analysis of the formant space of the three accents indicates that these accents are mostly conveyed by the first two formants. The third and fourth formants exhibit some significant differences across accents for only a few phonemes most notably the variants of vowel ‘r’ in the American (rhotic) accent compared to British (non-rhotic accent). The issue of speaker variability versus accent variability is examined by comparing the cross-entropies of speech models trained on different groups of speakers within and across the accents.

Most noisy speech enhancement methods result in partial suppression and distortion of speech spectrum. At instances when the local signal-to-noise ratio at a frequency band is very low speech partials are often obliterated. In this paper a method for enhancement and restoration of noisy speech based on a harmonic-noise model (HNM) is introduced. A HNM imposes a temporal-spectral structure that may reduce processing artifacts. The restoration process is enhanced through incorporation of a prior HNM of clean speech stored in a pre-trained codebook. The restored speech is a SNRdependent combination of the de-noised observation and the speech obtained from weighted codebook mapping. The additional improvements of speech quality resulting from the proposed method in comparison to conventional and modern speech enhancement systems are evaluated. The results show that the proposed method improves the quality of noisy speech and restores much of the information lost to noise.

This work proposes a novel method of predicting formant frequencies from a stream of mel-frequency cepstral coefficients (MFCC) feature vectors. Prediction is based on modelling the joint density of MFCCs and formant frequencies using a Gaussian mixture model (GMM). Using this GMM and an input MFCC vector, two maximum a posteriori (MAP) prediction methods are developed. The first method predicts formants from the closest, in some sense, cluster to the input MFCC vector, while the second method takes a weighted contribution of formants predicted from all clusters. Experimental results are presented using the ETSI Aurora connected digit database and show that predicted formant frequencies are within 3.2% of reference formant frequencies.

This paper presents a formant-tracking method for estimation of the time-varying trajectories of a linear prediction (LP) model of speech in noise. The main focus of this work is on the modelling of the non-stationary temporal trajectories of the formants of speech for improved LP model estimation in noise. The proposed approach provides a systematic framework for modelling the inter-frame correlation of speech parameters across successive frames, the intra-frame correlations are modelled by LP parameters. The formant-tracking LP model estimation is composed of two stages: (a) a pre-cleaning intra-frame spectral amplitude estimation stage where an initial estimate of the magnitude frequency response of the LP model of clean speech is obtained and (b) an inter-frame signal processing stage where formant classification and Kalman filters are combined to estimate the trajectory of formants. The effects of car and train noise on the observations and estimation of formants tracks are investigated. The average formant tracking errors at different signal to noise ratios (SNRs) are computed. The evaluation results demonstrate that after noise reduction and Kalman filtering the formant tracking errors are significantly reduced. 1.

This paper presents a voice conversion method based on transformation of the characteristic features of a source speaker towards a target. Voice characteristic features are grouped into two main categories: (a) the spectral features at formants and (b) the pitch and intonation patterns. Signal modelling and transformation methods for each group of voice features are outlined. The spectral features at formants are modelled using a set of two-dimensional phoneme-dependent HMM. Subband frequency warping is used for spectrum transformation with the subbands centred on the estimates of the formant trajectories. The F0 contour is used for modelling the pitch and intonation patterns of speech. A PSOLA based method is employed for transformation of pitch, intonation patterns and speaking rate. The experiments present illustrations and perceptual evaluations of the results of transformations of the various voice features.

This paper presents analysis through synthesis of the acoustic correlates of British, Australian and American accents by transforming the correlates individually across the accents. The acoustic correlates of accents are grouped into three main categories: (a) the spectral features at formants, (b) the pitch intonation pattern and (c) duration. The modeling and transformation methods for each group of voice features are outlined. The spectral features at formants are modeled using two-dimensional (2D) phoneme-dependent HMM. Subband frequency warping is used for spectrum transformation where the subbands are centred on estimates of the formant trajectories. The F0 contour is used for modeling the pitch and intonation patterns of speech. A method based on the time domain pitch synchronous overlap and add algorithm (TD-PSOLA) is used for transformation of pitch intonation and duration pattern. Perceptual tests based on mean opinion score (MOS) are conducted to rank the main features of accents. Formants are regarded as the most important features of accents, followed by intonation pattern and duration.

The differences between formants of British, Australian and American English accents are analysed and ranked. An improved formant model based on linear prediction (LP) feature analysis and a two-dimensional(2D) hidden Markov model (HMM) of formants is employed for estimation of the formant frequencies and bandwidths of vowels and diphthongs. Comparative analysis of the formant trajectories, the formant target points and the bandwidth of the spectral resonance at formants of British, Australian and American accents are presented. British vowels and diphthongs have smaller formant bandwidth than Australian. A method for ranking the contribution of different formants in conveying an accent is proposed whereby formants are ranked according to the normalized distances between the formants across accents. The first two formants are considered more sensitive to accents than other formants.

The formant space of three major English accents namely British, American and Australian are modelled and used for accent conversion. Accent synthesis, through modification of the acoustic parameters of speech, provides a means for assessing the perceptual contribution of each parameter on conveying an accent. An improved method based on a linear prediction (LP) model feature analysis and a 2D hidden Markov model (HMM) is employed for estimation of formant trajectories of vowels and diphthongs. Comparative analysis of the formant space of the three accents indicates that these accents are partly conveyed by the fronting and backing of vowels. It is found that the first formants of the vowels of British and American English accents are higher than those in Australian accent while Australians have higher second formants in vowels compared to Americans and British. The estimates of the distributions of formants for each accent are used in a speech synthesis system for accent conversion. Perceptual evaluations of accent conversion results illustrate that formants, in particular the second formant, play an important role in conveying accents.

This paper explores the estimation and mapping of probability models of formant parameter vectors for voice conversion. The formant parameter vectors consist of the frequency, bandwidth and intensity of resonance at formants. Formant parameters are derived from the coefficients of a linear prediction (LP) model of speech. The formant distributions are modelled with phonemedependent two-dimensional hidden Markov models with state Gaussian mixture densities. The HMMs are subsequently used for re-estimation of the formant trajectories of speech. Two alternative methods are explored for voice morphing. The first is a non-uniform frequency warping method and the second is based on spectral mapping via rotation of the formant vectors of the source towards those of the target. Both methods transform all formant parameters (Frequency, Bandwidth and Intensity). In addition, the factors that affect the selection of the warping ratios for the mapping function are presented. Experimental evaluation of voice morphing examples is presented.

Abstract The differences between the formant trajectories of British and broad Australian English accents are analysed and used for accent conversion. An improved formant model based on linear prediction (LP) feature analysis and a 2-D hidden Markov model (HMM) of formants is employed for estimation of the formant trajectories of vowels and diphthongs. Comparative analysis of the formant values, the formant trajectories and the formant target points of British and broad Australian accents are presented. A method for ranking the contribution of formants to accent identity is proposed whereby formants are ranked according to the normalised distances between formants across accents. The first two formants are considered more sensitive to accents than other formants. Finally a set of experiments on accent conversion is presented to transform the broad Australian accent of a speaker to British Received Pronunciation (RP) accent by formant mapping and prosody modification. Perceptual evaluations of accent conversion results illustrate that besides prosodic correlates such as pitch and duration, formants also play an important role in conveying accents.

In this paper, we present a comparative study of the acoustic speech features of two major English accents: British English and American English. Experiments examined the deterioration in speech recognition resulting from the mismatch between English accents of the input speech and the speech models. Mismatch in accents can increase the error rates by more than 100%. Hence a detailed study of the acoustic correlates of accent using intonation pattern and pitch characteristics was performed. Accents differences are acoustic manifestations of differences in duration, pitch and intonation pattern and of course the differences in phonetic transcriptions. Particularly, British speakers possess much steeper pitch rise and fall pattern and lower average pitch in most of vowels. Finally a possible means to convert English accents is suggested based on above analysis.