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Distance-Based Sound Separation
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  <p>&nbsp;</p>
  <p><span class="title">Distance-Based Sound Separation</span></p>
  <br />
  <table border="0" align="center" class="authors">
    <tr align="center">
      <td><NAME></td>
      <td><a href="https://research.google/people/KevinWilson"><NAME></a></td>
      <td><a href="https://research.google/people/ScottWisdom"><NAME></a></td>
      <td><a href="https://research.google/people/106072"><NAME></a></td>
    </tr>
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  <table border="0" align="center" class="affiliations">
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      <!--
      <td align="center"><img src="images/logo_research.png" height="40" alt=""/></td>
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      <td align="left"><a href="https://research.google.com/">Google Research</a></td>
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  <div align="center">
  <img  src="./images/distance_based_separation_figure1.png" alt="Distance-based sound separation block diagram" width="500">
  </div>
  <br />
  <p><span class="section">Abstract</span></p>
  <p>We propose the novel task of <em>distance-based sound separation,</em> where sounds are separated based only on their distance from a single microphone.  In the context of assisted listening devices, proximity provides a simple criterion for sound selection in noisy environments that would allow the user to focus on sounds relevant to a local conversation.  We demonstrate the feasibility of this approach by training a neural network to separate near sounds from far sounds in single channel synthetic reverberant mixtures, relative to a threshold distance defining the boundary between near and far.  With a single nearby speaker and four distant speakers, the model improves scale-invariant signal to noise ratio by 4.4 dB for near sounds and 6.8 dB for far sounds.
  </p>
  <p>&nbsp;</p>
  <p class="section">Paper</p>
  <table border="0">
    <tbody>
      <tr>
        <td>&nbsp;</td>
        <td><p>&quot;Distance-Based Sound Separation&quot;,<br />
            <NAME>, <NAME>, <NAME>, and <NAME>,<br />
            Proc. Interspeech, September 2022, Incheon, Korea.</a></p>
      </tr>
    </tbody>
  </table>
  <br />
  <p class="section">Real data audio demo</p>
<p>This example was recorded with an iPhone in a room of size approximately 4m by 5m, with near speaker approximately 0.5m from the microphone and the far speaker approximately 2m from the microphone.  The example was processed by a model trained as described in the paper with training data source presence probability of 0.5.</p>
<table>
  <tr><commit_msg>Update distance-based separation page with link to arXiv.<commit_after><!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>
Distance-Based Sound Separation
</title>
<link href="css/style.css" rel="stylesheet" type="text/css" />
</head>
<body>
<div class="container">
  <p>&nbsp;</p>
  <p><span class="title">Distance-Based Sound Separation</span></p>
  <br />
  <table border="0" align="center" class="authors">
    <tr align="center">
      <td><NAME></td>
      <td><a href="https://research.google/people/KevinWilson"><NAME></a></td>
      <td><a href="https://research.google/people/ScottWisdom"><NAME></a></td>
      <td><a href="https://research.google/people/106072"><NAME></a></td>
    </tr>
  </table>
  <table border="0" align="center" class="affiliations">
    <tr>
      <!--
      <td align="center"><img src="images/logo_research.png" height="40" alt=""/></td>
      -->
      <td align="left"><a href="https://research.google.com/">Google Research</a></td>
    </tr>
  </table>
  <br />
  <div align="center">
  <img  src="./images/distance_based_separation_figure1.png" alt="Distance-based sound separation block diagram" width="500">
  </div>
  <br />
  <p><span class="section">Abstract</span></p>
  <p>We propose the novel task of <em>distance-based sound separation,</em> where sounds are separated based only on their distance from a single microphone.  In the context of assisted listening devices, proximity provides a simple criterion for sound selection in noisy environments that would allow the user to focus on sounds relevant to a local conversation.  We demonstrate the feasibility of this approach by training a neural network to separate near sounds from far sounds in single channel synthetic reverberant mixtures, relative to a threshold distance defining the boundary between near and far.  With a single nearby speaker and four distant speakers, the model improves scale-invariant signal to noise ratio by 4.4 dB for near sounds and 6.8 dB for far sounds.
  </p>
  <p>&nbsp;</p>
  <p class="section">Paper</p>
  <table border="0">
    <tbody>
      <tr>
        <td>&nbsp;</td>
        <td><p>&quot;Distance-Based Sound Separation&quot;,<br />
            <NAME>, <NAME>, <NAME>, and <NAME>,<br />
            Proc. Interspeech, September 2022, Incheon, Korea.</a></p>
        <p>[<a href="http://arxiv.org/abs/2207.00562">PDF</a>]</p></td>
      </tr>
    </tbody>
  </table>
  <br />
  <p class="section">Real data audio demo</p>
<p>This example was recorded with an iPhone in a room of size approximately 4m by 5m, with near speaker approximately 0.5m from the microphone and the far speaker approximately 2m from the microphone.  The example was processed by a model trained as described in the paper with training data source presence probability of 0.5.</p>
<table>
  <tr>
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