valence_and_arousal_value.ipynb

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      "source": [
        "import librosa\n",
        "import numpy as np"
      ]
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    {
      "cell_type": "code",
      "source": [
        "# Load the audio file\n",
        "audio_file = \"/content/01-counting-stars.mp3\"\n",
        "audio_data, sr = librosa.load(audio_file)"
      ],
      "metadata": {
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "# Calculate the tempo and beat frames\n",
        "tempo, beat_frames = librosa.beat.beat_track(y = audio_data, sr = sr)\n",
        "#empo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)"
      ],
      "metadata": {
        "id": "thukRdbMevVu"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "# Calculate the spectral centroid and bandwidth\n",
        "spec_centroid = librosa.feature.spectral_centroid(y = audio_data, sr = sr)[0]\n",
        "spec_bandwidth = librosa.feature.spectral_bandwidth(y = audio_data, sr = sr)[0]"
      ],
      "metadata": {
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "# Calculate the RMS energy and zero-crossing rate\n",
        "rms_energy = librosa.feature.rms(y = audio_data)[0]\n",
        "zero_crossing_rate = librosa.feature.zero_crossing_rate(y = audio_data)[0]"
      ],
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean and standard deviation of the spectral centroid and bandwidth\n",
        "spec_centroid_mean = np.mean(spec_centroid)\n",
        "spec_centroid_std = np.std(spec_centroid)\n",
        "spec_bandwidth_mean = np.mean(spec_bandwidth)\n",
        "spec_bandwidth_std = np.std(spec_bandwidth)"
      ],
      "metadata": {
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean and standard deviation of the RMS energy and zero-crossing rate\n",
        "rms_energy_mean = np.mean(rms_energy)\n",
        "rms_energy_std = np.std(rms_energy)\n",
        "zero_crossing_rate_mean = np.mean(zero_crossing_rate)\n",
        "zero_crossing_rate_std = np.std(zero_crossing_rate)"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the valence and arousal values\n",
        "valence = (spec_centroid_mean - spec_centroid_std) + (rms_energy_mean + zero_crossing_rate_mean) / 2\n",
        "arousal = (spec_bandwidth_mean - spec_bandwidth_std) + (tempo + zero_crossing_rate_std) / 2"
      ],
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        "arousal"
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