BeatBot-Music-Genre-and-Singer-Classification-Website/about.html at main · rahulzach/BeatBot-Music-Genre-and-Singer-Classification-Website · GitHub

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<!DOCTYPE html>
<html lang="en">

<head>
  <meta charset="UTF-8">
  <meta http-equiv="X-UA-Compatible" content="IE=edge">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>About - BeatBot</title>
  <link rel="stylesheet" href="style.css">
  <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Inter:wght@400;700&display=swap">
</head>

<body>
  <header>
    <nav>
      <a href="home.html" class="logo">BeatBot</a>
      <ul>
        <li><a href="home.html">Home</a></li>
        <li><a href="about.html">About</a></li>
        <li><a href="help.html">Help</a></li>
        <li><a href="login.html">SignOut</a></li>
      </ul>
    </nav>
  </header>
  <main>
    <h1>About Us</h1>
    <p>Our mission is to provide music enthusiasts and professionals alike with a cutting-edge platform for analyzing and understanding audio content. By leveraging state-of-the-art machine learning algorithms and carefully curated datasets, we aim to empower users to explore and appreciate music in new and exciting ways.


    </p>
    <h2>What we do</h2>
    <p>Genre Recognition: Using the Gitzen dataset available on Kaggle, we have developed robust algorithms capable of accurately identifying the genre of any audio input. Whether it's rock, pop, jazz, or electronic, our system can analyze the sonic characteristics of a piece of music and determine its genre with precision.


    </p>
    <p>Artist Identification: In addition to genre recognition, we have curated our own dataset for artist identification. Our custom dataset contains a diverse selection of artists spanning various genres and musical styles. By harnessing the power of machine learning, we can match audio samples to the artists who performed them, providing users with valuable insights into the music they love.


    </p>


  </main>
</body>

</html>