08. Sounds Classification with pyAudioAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d01c7be0",
   "metadata": {},
   "source": [
    "# Acoustic Anomaly Detection based on Images with Azure AutoML for Images\n",
    "## 08. Sounds Classification with pyAudioAnalysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219adf84",
   "metadata": {},
   "source": [
    "<img src='https://github.com/retkowsky/CustomVision_Datasets/blob/master/anomaly.png?raw=true' width = 600>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55d1f08",
   "metadata": {},
   "source": [
    "Serge Retkowsky | serge.retkowsky@microsoft.com | 25-Oct-2022 | V3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e6d1d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pyAudioAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27e7c4c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install eyed3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "813966d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pyAudioAnalysis\n",
    "#!pip install eyed3\n",
    "#!pip install hmmlearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e0b2e7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\n",
      "  warn(\"Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "import plotly\n",
    "import IPython\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from pyAudioAnalysis import ShortTermFeatures as aF\n",
    "from pyAudioAnalysis import audioBasicIO as aIO\n",
    "from pyAudioAnalysis.audioTrainTest import extract_features_and_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a5ec81",
   "metadata": {},
   "source": [
    "https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d65ead9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KO', 'OK']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('audio_samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3011180b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Param = 20.00000 - classifier Evaluation Experiment 192 of 410\n",
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      "Param = 20.00000 - classifier Evaluation Experiment 201 of 410\n",
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      "Param = 20.00000 - classifier Evaluation Experiment 208 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 209 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 210 of 410\n",
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      "Param = 20.00000 - classifier Evaluation Experiment 216 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 217 of 410\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param = 20.00000 - classifier Evaluation Experiment 218 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 219 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 220 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 221 of 410\n",
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      "Param = 20.00000 - classifier Evaluation Experiment 343 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 344 of 410\n",
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      "Param = 20.00000 - classifier Evaluation Experiment 352 of 410\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param = 20.00000 - classifier Evaluation Experiment 353 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 354 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 355 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 356 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 357 of 410\n",
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      "Param = 20.00000 - classifier Evaluation Experiment 359 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 360 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 361 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 362 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 363 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 364 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 365 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 366 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 367 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 368 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 369 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 370 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 371 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 372 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 373 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 374 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 375 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 376 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 377 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 378 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 379 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 380 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 381 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 382 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 383 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 384 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 385 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 386 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 387 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 388 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 389 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 390 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 391 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 392 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 393 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 394 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 395 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 396 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 397 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 398 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 399 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 400 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 401 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 402 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 403 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 404 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 405 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 406 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 407 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 408 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 409 of 410\n",
      "Param = 20.00000 - classifier Evaluation Experiment 410 of 410\n",
      "\t\tok\t\t\tko\t\tOVERALL\n",
      "\tC\tPRE\tREC\tf1\tPRE\tREC\tf1\tACC\tf1\n",
      "\t0.001\t38.5\t36.9\t37.7\t39.8\t41.4\t40.6\t39.2\t39.1\n",
      "\t0.010\t39.3\t44.4\t41.7\t39.1\t34.2\t36.5\t39.2\t39.1\n",
      "\t0.500\t83.4\t95.9\t89.2\t95.2\t81.1\t87.6\t88.5\t88.4\n",
      "\t1.000\t85.5\t96.0\t90.5\t95.4\t83.9\t89.3\t89.9\t89.9\n",
      "\t5.000\t88.5\t92.0\t90.2\t91.9\t88.5\t90.2\t90.2\t90.2\t best f1\t best Acc\n",
      "\t10.000\t89.1\t87.5\t88.3\t87.5\t89.1\t88.3\t88.3\t88.3\n",
      "\t20.000\t90.0\t87.4\t88.7\t87.1\t89.8\t88.4\t88.6\t88.6\n",
      "Confusion Matrix:\n",
      "\tok\tko\n",
      "ok\t45.25\t3.96\n",
      "ko\t5.85\t44.93\n",
      "Best macro f1 90.2\n",
      "Best macro f1 std 8.4\n",
      "Selected params: 5.00000\n"
     ]
    }
   ],
   "source": [
    "# extract short-term features using a 50msec non-overlapping windows\n",
    "mt, st = 0.05, 0.05\n",
    "\n",
    "dirs = [\"audio_samples/ok\", \"audio_samples/ko\"]\n",
    "\n",
    "extract_features_and_train(dirs, mt, mt, st, st, \"svm_rbf\",\n",
    "                           \"machineanomalymodel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0e035fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[60  1]\n",
      " [ 5 56]]\n",
      "[0.9836065573770492, 0.9180327868852459] [0.9230769230769231, 0.9824561403508771] [0.9523809523809524, 0.9491525423728813] 0.9507667473769168 0.9508196721311475\n"
     ]
    }
   ],
   "source": [
    "from pyAudioAnalysis import audioTrainTest as aT\n",
    "\n",
    "cm, thr_prre, pre, rec, thr_roc, fpr, tpr = aT.evaluate_model_for_folders(\n",
    "    [\"audio_samples/ok/\", \"audio_samples/ko/\"], \"machineanomalymodel\",\n",
    "    \"svm_rbf\", \"ok\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a43eccef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>56</td>\n",
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      ],
      "text/plain": [
       "   c1  c2\n",
       "0  60   1\n",
       "1   5  56"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confmatrix = pd.DataFrame(cm, columns=['c1', 'c2'])\n",
    "confmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6065f964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.9508\n",
      "Precision = 0.9231\n",
      "Recall = 0.9836\n",
      "F1score = 0.9524\n"
     ]
    }
   ],
   "source": [
    "TP = confmatrix.iloc[0, 0]\n",
    "FN = confmatrix.iloc[0, 1]\n",
    "FP = confmatrix.iloc[1, 0]\n",
    "TN = confmatrix.iloc[1, 1]\n",
    "\n",
    "accuracy = (TP + TN) / (TP + TN + FN + FP)\n",
    "precision = TP / (TP + FP)\n",
    "recall = TP / (TP + FN)\n",
    "f1score = 2 * (recall * precision) / (recall + precision)\n",
    "\n",
    "print(\"Accuracy =\", round(accuracy, 4))\n",
    "print(\"Precision =\", round(precision, 4))\n",
    "print(\"Recall =\", round(recall, 4))\n",
    "print(\"F1score =\", round(f1score, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47c82f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "plt.clf()\n",
    "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "classNames = ['OK', 'KO']\n",
    "plt.title('Sound Anomaly Detection')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "tick_marks = np.arange(len(classNames))\n",
    "plt.xticks(tick_marks, classNames, rotation=45)\n",
    "plt.yticks(tick_marks, classNames)\n",
    "\n",
    "s = [['TP', 'FN'], ['FP', 'TP']]\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        plt.text(j, i, str(cm[i][j]))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "424726a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints', 'machine_KO.wav', 'machine_OK.wav', 'spectogram.jpg']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bdb91c03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysing test/machine_OK.wav\n",
      "\n",
      "\u001b[1;31;34mPredicted Class: OK\n",
      "Confidence = 0.97164\n"
     ]
    }
   ],
   "source": [
    "files_to_test = [\"test/machine_OK.wav\"]\n",
    "\n",
    "for f in files_to_test:\n",
    "    print('Analysing ' f'{f}')\n",
    "    c, p, p_nam = aT.file_classification(f, \"machineanomalymodel\",\n",
    "                                         \"gradientboosting\")\n",
    "    n = np.array(p)\n",
    "    maxindex = np.argmax(n)\n",
    "    predictedclass = p_nam[maxindex]\n",
    "    confidence = max(p)\n",
    "    print(\"\\n\\033[1;31;34mPredicted Class:\", str.upper(predictedclass))\n",
    "    print(\"Confidence =\", round(confidence, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ae5302e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysing test/machine_KO.wav\n",
      "\n",
      "\u001b[1;31;34mPredicted Class: KO\n",
      "Confidence = 0.93816\n"
     ]
    }
   ],
   "source": [
    "files_to_test = [\"test/machine_KO.wav\"]\n",
    "\n",
    "for f in files_to_test:\n",
    "    print('Analysing ' f'{f}')\n",
    "    c, p, p_nam = aT.file_classification(f, \"machineanomalymodel\",\n",
    "                                         \"gradientboosting\")\n",
    "\n",
    "    n = np.array(p)\n",
    "    maxindex = np.argmax(n)\n",
    "    predictedclass = p_nam[maxindex]\n",
    "    confidence = max(p)\n",
    "    print(\"\\n\\033[1;31;34mPredicted Class:\", str.upper(predictedclass))\n",
    "    print(\"Confidence =\", round(confidence, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c439d5f",
   "metadata": {},
   "source": [
    "Note: All these Python notebooks were made for demo purposes. They were not designed for production usage. Some feature can be currently in public preview. This preview version is provided without a service-level agreement. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews. https://azure.microsoft.com/en-us/support/legal/preview-supplemental-terms/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f45edc92",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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