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highPassFilter.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b2d45592-5695-40ec-ad90-7d03275337a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import scipy.signal\n",
+    "\n",
+    "# def high_pass_filter(audio, sr, cutoff=200, order=3):\n",
+    "#     \"\"\"\n",
+    "#     Applies a high-pass filter to an audio signal.\n",
+    "\n",
+    "#     Parameters:\n",
+    "#     audio (numpy array): The input audio signal.\n",
+    "#     sr (int): The sample rate of the audio signal.\n",
+    "#     cutoff (float): The cutoff frequency in Hz. Default is 100 Hz.\n",
+    "#     order (int): The order of the filter. Default is 5.\n",
+    "    \n",
+    "#     Returns:\n",
+    "#     numpy array: The filtered audio signal.\n",
+    "#     \"\"\"\n",
+    "#     # Design the high-pass filter using a Butterworth filter design\n",
+    "#     sos = scipy.signal.butter(order, cutoff, btype='highpass', fs=sr, output='sos')\n",
+    "    \n",
+    "#     # Apply the filter using sosfilt (second-order sections filter)\n",
+    "#     filtered_audio = scipy.signal.sosfilt(sos, audio)\n",
+    "    \n",
+    "#     return filtered_audio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a1efa78-61d4-4545-b50a-080161bb6aa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def high_pass_filter(audio, sr, cutoff=300):\n",
+    "    # Design a Butterworth high-pass filter\n",
+    "    nyquist = 0.5 * sr\n",
+    "    normal_cutoff = cutoff / nyquist\n",
+    "    b, a = butter(1, normal_cutoff, btype='high', analog=False)\n",
+    "    filtered_audio = lfilter(b, a, audio)\n",
+    "    return filtered_audio"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

waveletDenoise.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5d909ed5-71b2-4586-96e1-f7820a8912ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to apply wavelet denoising\n",
+    "def wavelet_denoise(audio, wavelet='db1', level=1):\n",
+    "    coeffs = pywt.wavedec(audio, wavelet, mode='per')\n",
+    "    # Thresholding detail coefficients\n",
+    "    sigma = np.median(np.abs(coeffs[-level])) / 0.6745\n",
+    "    uthresh = sigma * np.sqrt(2 * np.log(len(audio)))\n",
+    "    coeffs[1:] = [pywt.threshold(i, value=uthresh, mode='soft') for i in coeffs[1:]]\n",
+    "    return pywt.waverec(coeffs, wavelet, mode='per')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02d29b97-fe10-4cd9-a176-0c7bf153a3f9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

wienerFilter.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a680a4b0-f69e-4645-9790-2f70f2bcf48f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.signal\n",
+    "def wiener_filter(audio):\n",
+    "    \n",
+    "    '''\n",
+    "    The Wiener filter is designed to minimize the impact of noise by applying an adaptive filtering process. \n",
+    "    It tries to estimate the original, clean signal by taking into account both the noisy signal and the statistical properties of the noise. \n",
+    "    The Wiener filter is particularly useful when dealing with stationary noise (constant background noise, like white noise).\n",
+    "    '''\n",
+    "    return scipy.signal.wiener(audio)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4eaa3f1-770f-4260-8a84-0d7afe5ea3b4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}