Delete wienerFilter.ipynb

wienerFilter.ipynb

@@ -1,51 +0,0 @@
-{
- "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
-}