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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Music Genre Classification Project</title>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap" rel="stylesheet">
<style>
body { font-family: 'Inter', sans-serif; }
.glass { backdrop-filter: blur(10px); background: rgba(255,255,255,0.78); }
.gradient { background: linear-gradient(135deg, #0f172a 0%, #1d4ed8 55%, #06b6d4 100%); }
pre { white-space: pre-wrap; }
.code-card { background: #0f172a; color: #e2e8f0; }
</style>
</head>
<body class="bg-slate-50 text-slate-800">
<header class="gradient text-white">
  <div class="max-w-6xl mx-auto px-6 py-20">
    <p class="uppercase tracking-[0.3em] text-cyan-200 text-sm mb-4">Deep Learning Project</p>
    <h1 class="text-5xl md:text-6xl font-extrabold leading-tight">Music Genre Classification</h1>
    <p class="mt-6 text-xl text-slate-100 max-w-3xl leading-8">
      A robust end-to-end audio classification system that predicts one of ten music genres from raw audio.
      The pipeline converts audio to mel spectrograms, injects realistic environmental noise from ESC-50,
      trains multiple deep learning architectures, and combines the strongest models using a weighted ensemble
      with test-time augmentation.
    </p>
    <div class="mt-8 flex flex-wrap gap-3">
      <span class="px-4 py-2 rounded-full bg-white/10 border border-white/20">10 Genres</span>
      <span class="px-4 py-2 rounded-full bg-white/10 border border-white/20">ESC-50 Noise Augmentation</span>
      <span class="px-4 py-2 rounded-full bg-white/10 border border-white/20">EfficientNet-B0</span>
      <span class="px-4 py-2 rounded-full bg-white/10 border border-white/20">AST Transformer</span>
      <span class="px-4 py-2 rounded-full bg-white/10 border border-white/20">Weighted Ensemble + TTA</span>
    </div>
  </div>
</header>

<main class="max-w-6xl mx-auto px-6 py-12 space-y-20">
  <section class="grid md:grid-cols-4 gap-6 -mt-20">
    <div class="glass rounded-3xl shadow-xl p-6"><div class="text-3xl font-extrabold text-blue-600">3</div><div class="text-sm text-slate-600 mt-1">Models Trained</div></div>
    <div class="glass rounded-3xl shadow-xl p-6"><div class="text-3xl font-extrabold text-cyan-600">50</div><div class="text-sm text-slate-600 mt-1">Noise Classes</div></div>
    <div class="glass rounded-3xl shadow-xl p-6"><div class="text-3xl font-extrabold text-indigo-600">224×224</div><div class="text-sm text-slate-600 mt-1">Spectrogram Size</div></div>
    <div class="glass rounded-3xl shadow-xl p-6"><div class="text-3xl font-extrabold text-emerald-600">95%</div><div class="text-sm text-slate-600 mt-1">Estimated Ensemble F1</div></div>
  </section>

  <section>
    <h2 class="text-3xl font-bold mb-4">Project Overview</h2>
    <p class="text-lg text-slate-600 leading-8">
      This project explores how modern computer vision and transformer architectures can be applied to audio.
      By representing music as mel spectrograms, the classification task becomes an image understanding problem.
      To improve robustness, environmental sounds such as rain, traffic, and crowd noise are mixed into training
      samples using the ESC-50 dataset. The final system combines EfficientNet-B0 and Audio Spectrogram Transformer
      (AST) predictions with weights based on validation F1 scores.
    </p>
  </section>

  <section>
    <h2 class="text-3xl font-bold mb-8">Pipeline Architecture</h2>
    <div class="grid md:grid-cols-5 gap-4 text-center">
      <div class="bg-white rounded-2xl p-5 shadow">Raw Audio</div>
      <div class="bg-white rounded-2xl p-5 shadow">Noise Injection</div>
      <div class="bg-white rounded-2xl p-5 shadow">Mel Spectrogram</div>
      <div class="bg-white rounded-2xl p-5 shadow">Model Training</div>
      <div class="bg-white rounded-2xl p-5 shadow">Weighted Ensemble</div>
    </div>
  </section>

  <section>
    <h2 class="text-3xl font-bold mb-8">Sample Spectrograms</h2>
    <p class="text-slate-600 mb-6 leading-7">
      Below are representative spectrogram visualizations illustrating how different genres exhibit unique
      time-frequency patterns that the models learn to distinguish.
    </p>
    <div class="grid md:grid-cols-3 gap-6">
      <img src="classical.png" class="rounded-2xl shadow-lg" alt="Classical spectrogram">
      <img src="rock.png" class="rounded-2xl shadow-lg" alt="Rock spectrogram">
      <img src="jazz.png" class="rounded-2xl shadow-lg" alt="Jazz spectrogram">
    </div>
  </section>

  <section>
    <h2 class="text-3xl font-bold mb-8">Model Architectures</h2>
    <div class="grid md:grid-cols-3 gap-6">
      <div class="bg-white rounded-3xl p-6 shadow-lg border border-slate-100">
        <h3 class="text-xl font-bold mb-3">Scratch CNN</h3>
        <p class="text-slate-600 leading-7">A custom convolutional baseline with four feature extraction blocks and a compact classifier head.</p>
      </div>
      <div class="bg-white rounded-3xl p-6 shadow-lg border border-slate-100">
        <h3 class="text-xl font-bold mb-3">EfficientNet-B0</h3>
        <p class="text-slate-600 leading-7">A pretrained vision model adapted to single-channel spectrogram inputs for efficient transfer learning.</p>
      </div>
      <div class="bg-white rounded-3xl p-6 shadow-lg border border-slate-100">
        <h3 class="text-xl font-bold mb-3">Audio Spectrogram Transformer</h3>
        <p class="text-slate-600 leading-7">A transformer architecture pretrained on AudioSet that captures long-range temporal dependencies.</p>
      </div>
    </div>
  </section>

  <section class="space-y-8">
    <h2 class="text-3xl font-bold">Key Code Snippets</h2>

    <div class="code-card rounded-3xl p-6 shadow-xl">
      <h3 class="text-xl font-bold mb-3 text-white">Audio to Mel Spectrogram</h3>
<pre><code>def audio_to_mel(y, sr=22050, n_mels=224):
    mel = librosa.feature.melspectrogram(
        y=y, sr=sr, n_mels=n_mels,
        n_fft=2048, hop_length=512
    )
    mel_db = librosa.power_to_db(mel, ref=np.max)
    return resize_to_224x224(mel_db)</code></pre>
    </div>

    <div class="code-card rounded-3xl p-6 shadow-xl">
      <h3 class="text-xl font-bold mb-3 text-white">ESC-50 Noise Augmentation</h3>
<pre><code>def add_noise(audio, noise_clip, snr_db=10):
    signal_power = np.mean(audio ** 2)
    noise_power = np.mean(noise_clip ** 2)
    factor = np.sqrt(signal_power / (10 ** (snr_db / 10) * noise_power))
    return audio + factor * noise_clip</code></pre>
    </div>

    <div class="code-card rounded-3xl p-6 shadow-xl">
      <h3 class="text-xl font-bold mb-3 text-white">EfficientNet-B0 Definition</h3>
<pre><code>model = timm.create_model(
    "efficientnet_b0",
    pretrained=True,
    in_chans=1,
    num_classes=10
)</code></pre>
    </div>

    <div class="code-card rounded-3xl p-6 shadow-xl">
      <h3 class="text-xl font-bold mb-3 text-white">Audio Spectrogram Transformer</h3>
<pre><code>ast = ASTForAudioClassification.from_pretrained(
    "MIT/ast-finetuned-audioset-10-10-0.4593",
    num_labels=10,
    ignore_mismatched_sizes=True
)</code></pre>
    </div>

    <div class="code-card rounded-3xl p-6 shadow-xl">
      <h3 class="text-xl font-bold mb-3 text-white">Weighted Ensemble</h3>
<pre><code>w_eff = f1_eff / (f1_eff + f1_ast)
w_ast = f1_ast / (f1_eff + f1_ast)

final_probs = w_eff * eff_probs + w_ast * ast_probs
prediction = np.argmax(final_probs, axis=1)</code></pre>
    </div>
  </section>

  <section class="grid lg:grid-cols-2 gap-10 items-start">
    <div>
      <h2 class="text-3xl font-bold mb-4">Estimated Validation F1 Score</h2>
      <p class="text-slate-600 leading-7 mb-6">
        EfficientNet-B0 and AST outperform the scratch CNN and achieve the best results when combined.
      </p>
      <canvas id="f1Chart"></canvas>
    </div>
    <div>
      <h2 class="text-3xl font-bold mb-4">Training Configuration</h2>
      <div class="bg-white rounded-3xl p-6 shadow-lg">
        <ul class="space-y-3 text-slate-700">
          <li><strong>Split:</strong> 85/15 stratified</li>
          <li><strong>Mixup:</strong> α = 0.4</li>
          <li><strong>Noise Source:</strong> ESC-50</li>
          <li><strong>EfficientNet LR:</strong> 2e-4</li>
          <li><strong>AST LR:</strong> 5e-5</li>
          <li><strong>TTA:</strong> Multiple noisy passes</li>
        </ul>
      </div>
    </div>
  </section>

  <section>
    <h2 class="text-3xl font-bold mb-8">Technologies Used</h2>
    <div class="flex flex-wrap gap-3">
      <span class="px-4 py-2 bg-white rounded-full shadow">Python</span>
      <span class="px-4 py-2 bg-white rounded-full shadow">PyTorch</span>
      <span class="px-4 py-2 bg-white rounded-full shadow">Librosa</span>
      <span class="px-4 py-2 bg-white rounded-full shadow">Transformers</span>
      <span class="px-4 py-2 bg-white rounded-full shadow">timm</span>
      <span class="px-4 py-2 bg-white rounded-full shadow">Kaggle</span>
    </div>
  </section>
</main>

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    Built from the original Jupyter notebook and optimized for deployment as a static Hugging Face Space.
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