ICASSP

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\title{Low-Rank Plus Sparse Dictionary Learning for\\Robust Speech Enhancement in Non-Stationary Noise}

\author{\IEEEauthorblockN{First Last, Jane Doe, John Smith}
\IEEEauthorblockA{\textit{Department of Electrical Engineering, University of Example}\\
\{you, jane, john\}@example.com}}
\maketitle

\begin{abstract}
We propose a low-rank plus sparse dictionary learning method for robust
speech enhancement in non-stationary noise. The method decomposes noisy
spectrograms into structured speech and unstructured noise via joint
optimization. On CHiME-6, our method improves PESQ by 0.24 and SDR by
3.1 dB over spectral-masking baselines while remaining training-free.
\end{abstract}

\begin{IEEEkeywords}
speech enhancement, dictionary learning, low rank, sparse coding
\end{IEEEkeywords}

\section{Introduction}
Non-stationary noise defeats classical spectral subtraction and stresses
deep models trained on stationary corpora.

\section{Method}
We model the noisy magnitude spectrogram $Y$ as
\begin{equation}
  Y = L + S + N,
\end{equation}
where $L$ is low-rank speech, $S$ sparse transients, $N$ residual noise.
We solve the joint optimization via ADMM.

\section{Experiments}
\begin{table}[t]
\centering\small
\begin{tabular}{lcc}
\toprule
Method & PESQ & SDR (dB) \\
\midrule
Noisy baseline         & 1.62 & $-$1.8 \\
Spectral subtraction   & 1.89 &  2.4 \\
OMLSA                  & 2.13 &  4.1 \\
\textbf{Ours}          & \textbf{2.37} & \textbf{7.2} \\
\bottomrule
\end{tabular}
\caption{CHiME-6 evaluation set.}
\end{table}

\section{Conclusion}
Explicit low-rank plus sparse modeling captures speech structure well
and generalizes across noise conditions.

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