New versions of the all-ones problem

We study three new versions of the All-Ones Problem and the Minimum All-Ones Problem. The original All-Ones Problem is simply called the Vertex-Vertex Problem, and the three new versions are called the Vertex-Edge Problem, the Edge-Vertex Problem and the Edge-Edge Problem, respectively. The Vertex-Vertex Problem has been studied extensively. For example, existence of solutions and efficient algorithms for finding solutions were obtained, and the Minimum Vertex-Vertex Problem for general graphs was shown to be NP-complete and for trees it can be solved in linear time, etc. In this paper, for the Vertex-Edge Problem, we show that a graph has a solution if and only if it is bipartite, and therefore it has only two possible solutions and optimal solutions. A linear program version is also given. For the Edge-Vertex Problem, we show that a graph has a solution if and only if it contains even number of vertices. By showing that the Minimum Edge-Vertex Problem can be polynomially transformed into the Minimum Weight Perfect Matching Problem, we obtain that the Minimum Edge-Vertex Problem can be solved in polynomial time in general. The Edge-Edge Problem is reduced to the Vertex-Vertex Problem for the line graph of a graph.Comment: 12 page

Investigation of Monaural Front-End Processing for Robust ASR without Retraining or Joint-Training

In recent years, monaural speech separation has been formulated as a supervised learning problem, which has been systematically researched and shown the dramatical improvement of speech intelligibility and quality for human listeners. However, it has not been well investigated whether the methods can be employed as the front-end processing and directly improve the performance of a machine listener, i.e., an automatic speech recognizer, without retraining or joint-training the acoustic model. In this paper, we explore the effectiveness of the independent front-end processing for the multi-conditional trained ASR on the CHiME-3 challenge. We find that directly feeding the enhanced features to ASR can make 36.40% and 11.78% relative WER reduction for the GMM-based and DNN-based ASR respectively. We also investigate the affect of noisy phase and generalization ability under unmatched noise condition.Comment: 5 pages, 0 figures, 4 tables, conferenc

Rainbow vertex-connection and forbidden subgraphs

A path in a vertex-colored graph is called \emph{vertex-rainbow} if its internal vertices have pairwise distinct colors. A graph $G$ is \emph{rainbow vertex-connected} if for any two distinct vertices of $G$, there is a vertex-rainbow path connecting them. For a connected graph $G$, the \emph{rainbow vertex-connection number} of $G$, denoted by $rvc(G)$, is defined as the minimum number of colors that are required to make $G$ rainbow vertex-connected. In this paper, we find all the families $\mathcal{F}$ of connected graphs with $|\mathcal{F}|\in\{1,2\}$, for which there is a constant $k_\mathcal{F}$ such that, for every connected $\mathcal{F}$-free graph $G$, $rvc(G)\leq diam(G)+k_\mathcal{F}$, where $diam(G)$ is the diameter of $G$.Comment: 11 page

Good upper bounds for the total rainbow connection of graphs

A total-colored graph is a graph $G$ such that both all edges and all vertices of $G$ are colored. A path in a total-colored graph $G$ is a total rainbow path if its edges and internal vertices have distinct colors. A total-colored graph $G$ is total-rainbow connected if any two vertices of $G$ are connected by a total rainbow path of $G$. The total rainbow connection number of $G$, denoted by $trc(G)$, is defined as the smallest number of colors that are needed to make $G$ total-rainbow connected. These concepts were introduced by Liu et al. Notice that for a connected graph $G$, $2diam(G)-1\leq trc(G)\leq 2n-3$, where $diam(G)$ denotes the diameter of $G$ and $n$ is the order of $G$. In this paper we show, for a connected graph $G$ of order $n$ with minimum degree $\delta$, that $trc(G)\leq6n/{(\delta+1)}+28$ for $\delta\geq\sqrt{n-2}-1$ and $n\geq 291$, while $trc(G)\leq7n/{(\delta+1)}+32$ for $16\leq\delta\leq\sqrt{n-2}-2$ and $trc(G)\leq7n/{(\delta+1)}+4C(\delta)+12$ for $6\leq\delta\leq15$, where $C(\delta)=e^{\frac{3\log({\delta}^3+2{\delta}^2+3)-3(\log3-1)}{\delta-3}}-2$. This implies that when $\delta$ is in linear with $n$, then the total rainbow number $trc(G)$ is a constant. We also show that $trc(G)\leq 7n/4-3$ for $\delta=3$, $trc(G)\leq8n/5-13/5$ for $\delta=4$ and $trc(G)\leq3n/2-3$ for $\delta=5$. Furthermore, an example shows that our bound can be seen tight up to additive factors when $\delta\geq\sqrt{n-2}-1$.Comment: 8 page

Total monochromatic connection of graphs

A graph is said to be {\it total-colored} if all the edges and the vertices of the graph are colored. A path in a total-colored graph is a {\it total monochromatic path} if all the edges and internal vertices on the path have the same color. A total-coloring of a graph is a {\it total monochromatically-connecting coloring} ({\it TMC-coloring}, for short) if any two vertices of the graph are connected by a total monochromatic path of the graph. For a connected graph $G$, the {\it total monochromatic connection number}, denoted by $tmc(G)$, is defined as the maximum number of colors used in a TMC-coloring of $G$. These concepts are inspired by the concepts of monochromatic connection number $mc(G)$, monochromatic vertex connection number $mvc(G)$ and total rainbow connection number $trc(G)$ of a connected graph $G$. Let $l(T)$ denote the number of leaves of a tree $T$, and let $l(G)=\max\{ l(T) |$ $T$ is a spanning tree of $G$ $\}$ for a connected graph $G$. In this paper, we show that there are many graphs $G$ such that $tmc(G)=m-n+2+l(G)$, and moreover, we prove that for almost all graphs $G$, $tmc(G)=m-n+2+l(G)$ holds. Furthermore, we compare $tmc(G)$ with $mvc(G)$ and $mc(G)$, respectively, and obtain that there exist graphs $G$ such that $tmc(G)$ is not less than $mvc(G)$ and vice versa, and that $tmc(G)=mc(G)+l(G)$ holds for almost all graphs. Finally, we prove that $tmc(G)\leq mc(G)+mvc(G)$, and the equality holds if and only if $G$ is a complete graph.Comment: 12 page

Total proper connection of graphs

A graph is said to be {\it total-colored} if all the edges and the vertices of the graph is colored. A path in a total-colored graph is a {\it total proper path} if $(i)$ any two adjacent edges on the path differ in color, $(ii)$ any two internal adjacent vertices on the path differ in color, and $(iii)$ any internal vertex of the path differs in color from its incident edges on the path. A total-colored graph is called {\it total-proper connected} if any two vertices of the graph are connected by a total proper path of the graph. For a connected graph $G$, the {\it total proper connection number} of $G$, denoted by $tpc(G)$, is defined as the smallest number of colors required to make $G$ total-proper connected. These concepts are inspired by the concepts of proper connection number $pc(G)$, proper vertex connection number $pvc(G)$ and total rainbow connection number $trc(G)$ of a connected graph $G$. In this paper, we first determine the value of the total proper connection number $tpc(G)$ for some special graphs $G$. Secondly, we obtain that $tpc(G)\leq 4$ for any $2$-connected graph $G$ and give examples to show that the upper bound $4$ is sharp. For general graphs, we also obtain an upper bound for $tpc(G)$. Furthermore, we prove that $tpc(G)\leq \frac{3n}{\delta+1}+1$ for a connected graph $G$ with order $n$ and minimum degree $\delta$. Finally, we compare $tpc(G)$ with $pvc(G)$ and $pc(G)$, respectively, and obtain that $tpc(G)>pvc(G)$ for any nontrivial connected graph $G$, and that $tpc(G)$ and $pc(G)$ can differ by $t$ for $0\leq t\leq 2$.Comment: 15 page

Integrated Speech Enhancement Method Based on Weighted Prediction Error and DNN for Dereverberation and Denoising

Both reverberation and additive noises degrade the speech quality and intelligibility. Weighted prediction error (WPE) method performs well on the dereverberation but with limitations. First, WPE doesn't consider the influence of the additive noise which degrades the performance of dereverberation. Second, it relies on a time-consuming iterative process, and there is no guarantee or a widely accepted criterion on its convergence. In this paper, we integrate deep neural network (DNN) into WPE for dereverberation and denoising. DNN is used to suppress the background noise to meet the noise-free assumption of WPE. Meanwhile, DNN is applied to directly predict spectral variance of the target speech to make the WPE work without iteration. The experimental results show that the proposed method has a significant improvement in speech quality and runs fast

Using Optimal Ratio Mask as Training Target for Supervised Speech Separation

Supervised speech separation uses supervised learning algorithms to learn a mapping from an input noisy signal to an output target. With the fast development of deep learning, supervised separation has become the most important direction in speech separation area in recent years. For the supervised algorithm, training target has a significant impact on the performance. Ideal ratio mask is a commonly used training target, which can improve the speech intelligibility and quality of the separated speech. However, it does not take into account the correlation between noise and clean speech. In this paper, we use the optimal ratio mask as the training target of the deep neural network (DNN) for speech separation. The experiments are carried out under various noise environments and signal to noise ratio (SNR) conditions. The results show that the optimal ratio mask outperforms other training targets in general

Tight Nordhaus-Gaddum-type upper bound for total-rainbow connection number of graphs

A graph is said to be \emph{total-colored} if all the edges and the vertices of the graph are colored. A total-colored graph is \emph{total-rainbow connected} if any two vertices of the graph are connected by a path whose edges and internal vertices have distinct colors. For a connected graph $G$, the \emph{total-rainbow connection number} of $G$, denoted by $trc(G)$, is the minimum number of colors required in a total-coloring of $G$ to make $G$ total-rainbow connected. In this paper, we first characterize the graphs having large total-rainbow connection numbers. Based on this, we obtain a Nordhaus-Gaddum-type upper bound for the total-rainbow connection number. We prove that if $G$ and $\overline{G}$ are connected complementary graphs on $n$ vertices, then $trc(G)+trc(\overline{G})\leq 2n$ when $n\geq 6$ and $trc(G)+trc(\overline{G})\leq 2n+1$ when $n=5$. Examples are given to show that the upper bounds are sharp for $n\geq 5$. This completely solves a conjecture in [Y. Ma, Total rainbow connection number and complementary graph, Results in Mathematics 70(1-2)(2016), 173-182].Comment: 20 page

On (strong) proper vertex-connection of graphs

A path in a vertex-colored graph is a {\it vertex-proper path} if any two internal adjacent vertices differ in color. A vertex-colored graph is {\it proper vertex $k$-connected} if any two vertices of the graph are connected by $k$ disjoint vertex-proper paths of the graph. For a $k$-connected graph $G$, the {\it proper vertex $k$-connection number} of $G$, denoted by $pvc_{k}(G)$, is defined as the smallest number of colors required to make $G$ proper vertex $k$-connected. A vertex-colored graph is {\it strong proper vertex-connected}, if for any two vertices $u,v$ of the graph, there exists a vertex-proper $u$-$v$ geodesic. For a connected graph $G$, the {\it strong proper vertex-connection number} of $G$, denoted by $spvc(G)$, is the smallest number of colors required to make $G$ strong proper vertex-connected. These concepts are inspired by the concepts of rainbow vertex $k$-connection number $rvc_k(G)$, strong rainbow vertex-connection number $srvc(G)$, and proper $k$-connection number $pc_k(G)$ of a $k$-connected graph $G$. Firstly, we determine the value of $pvc(G)$ for general graphs and $pvc_k(G)$ for some specific graphs. We also compare the values of $pvc_k(G)$ and $pc_k(G)$. Then, sharp bounds of $spvc(G)$ are given for a connected graph $G$ of order $n$, that is, $0\leq spvc(G)\leq n-2$. Moreover, we characterize the graphs of order $n$ such that $spvc(G)=n-2,n-3$, respectively. Finally, we study the relationship among the three vertex-coloring parameters, namely, $spvc(G), \ srvc(G)$and the chromatic number$\chi(G)$of a connected graph$G$.Comment: 12 page