\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{A Flow-Based Deep Latent Variable Model for Speech Spectrogram Modeling
and Enhancement}



\author[1]{Aditya Arie Nugraha }%
\author[1]{Kouhei Sekiguchi }%
\author[1]{Kazuyoshi Yoshii }%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
This paper describes a deep latent variable model of speech power
spectrograms and its application to semi-supervised speech enhancement
with a deep speech prior. By integrating two major deep generative
models, a variational autoencoder (VAE) and a normalizing flow (NF), in
a mutually-beneficial manner, we formulate a flexible latent variable
model called the NF-VAE that can extract low-dimensional latent
representations from high-dimensional observations, akin to the VAE, and
does not need to explicitly represent the distribution of the
observations, akin to the NF. In this paper, we consider a variant of NF
called the generative flow (GF a.k.a. Glow) and formulate a latent
variable model called the GF-VAE. We experimentally show that the
proposed GF-VAE is better than the standard VAE at capturing
fine-structured harmonics of speech spectrograms, especially in the
high-frequency range. A similar finding is also obtained when the GF-VAE
and the VAE are used to generate speech spectrograms from latent
variables randomly sampled from the standard Gaussian distribution.
Lastly, when these models are used as speech priors for statistical
multichannel speech enhancement, the GF-VAE outperforms the VAE and the
GF.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

