Delete waveletDenoise.ipynb

waveletDenoise.ipynb

@@ -1,50 +0,0 @@
-{
- "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
-}