Singing voice correction using canonical time warping

Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solution. We thereby propose to address the problem by canonical time warping (CTW) which aligns amateur singing recordings to professional ones. A new pitch contour is generated given the alignment information, and a pitch-corrected singing is synthesized back through the vocoder. The objective evaluation shows that CTW is robust against pitch-shifting and time-stretching effects, and the subjective test demonstrates that CTW prevails the other methods including DTW and the commercial auto-tuning software. Finally, we demonstrate the applicability of the proposed method in a practical, real-world scenario

Factoring a Quadratic Operator as a Product of Two Positive Contractions

Let T be a quadratic operator on a complex Hilbert space H. We show that T can be written as a product of two positive contractions if and only if T is of the form aI circle plus bI circle plus [ GRAPHICS ] on H-1 circle plus H-2 circle plus (H-3 circle plus H-3) for some a, b is an element of [ 0, 1 ] and strictly positive operator P with parallel to P parallel to \u3c = vertical bar root a - root b vertical bar root (1 - a) (1 - b). Also, we give a necessary condition for a bounded linear operator T with operator matrix [GRAPHICS] on H circle plus K that can be written as a product of two positive contractions

Metabolic Pathways Enhancement Confers Poor Prognosis in p53 Exon Mutant Hepatocellular Carcinoma.

RNA-Sequencing (RNA-Seq), the most commonly used sequencing application tool, is not only a method for measuring gene expression but also an excellent media to detect important structural variants such as single nucleotide variants (SNVs), insertion/deletion (Indels), or fusion transcripts. The Cancer Genome Atlas (TCGA) contains genomic data from a variety of cancer types and also provides the raw data generated by TCGA consortium. p53 is among the top 10 somatic mutations associated with hepatocellular carcinoma (HCC). The aim of the present study was to analyze concordant different gene profiles and the priori defined set of genes based on p53 mutation status in HCC using RNA-Seq data. In the study, expression profile of 11 799 genes on 42 paired tumor and adjacent normal tissues was collected, processed, and further stratified by the mutated versus normal p53 expression. Furthermore, we used a knowledge-based approach Gene Set Enrichment Analysis (GSEA) to compare between normal and p53 mutation gene expression profiles. The statistical significance (nominal P value) of the enrichment score (ES) genes was calculated. The ranked gene list that reflects differential expression between p53 wild-type and mutant genotypes was then mapped to metabolic process by KEGG, an encyclopedia of genes and genomes to assign functional meanings. These approaches enable us to identify pathways and potential target gene/pathways that are highly expressed in p53 mutated HCC. Our analysis revealed 2 genes, the hexokinase 2 (HK2) and Enolase 1 (ENO1), were conspicuous of red pixel in the heatmap. To further explore the role of these genes in HCC, the overall survival plots by Kaplan-Meier method were performed for HK2 and ENO1 that revealed high HK2 and ENO1 expression in patients with HCC have poor prognosis. These results suggested that these glycolysis genes are associated with mutated-p53 in HCC that may contribute to poor prognosis. In this proof-of-concept study, we proposed an approach for identifying novel potential therapeutic targets in human HCC with mutated p53. These approaches can take advantage of the massive next-generation sequencing (NGS) data generated worldwide and make more out of it by exploring new potential therapeutic targets

Big Data Measures of Environmental Concern

Environmental concern is a subjective state of society and researchers have typically relied on survey data to measure it. However, survey-based methods only capture a snapshot of it at the time and place the surveys were conducted. To overcome these problems, we develop an observable indicator that allows us to study environmental concern over time and across territories based on big data. The indicator composes of keyword groups that fit with the environmental concern measures revealed by a large-scale survey. We find that keywords associated with climate change, water pollution and waste management are the strongest predictors of environmental concern. To the best of our knowledge, our paper is the first to use online search data to capture subjective environmental concern

Dual residence time for droplet to coalesce with liquid surface

When droplets approach a liquid surface, they have a tendency to merge in order to minimize surface energy. However, under certain conditions, they can exhibit a phenomenon called coalescence delay, where they remain separate for tens of milliseconds. This duration is known as the residence time or the non-coalescence time. Surprisingly, under identical parameters and initial conditions, the residence time for water droplets is not a constant value but exhibits dual peaks in its distribution. In this paper, we present the observation of the dual residence times through rigorous statistical analysis and investigate the quantitative variations in residence time by manipulating parameters such as droplet height, radius, and viscosity. Theoretical models and physical arguments are provided to explain their effects, particularly why a large viscosity or/and a small radius is detrimental to the appearance of the longer residence time peak.Comment: 7 pages, 6 figure