diff --git a/SoundProject.ipynb b/SoundProject.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import librosa\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.multiclass import OneVsRestClassifier\n",
+ "import matplotlib.pyplot as plt\n",
+ "import soundfile as sf\n",
+ "from sklearn.metrics import classification_report\n",
+ "from keras.models import Sequential\n",
+ "from keras.utils import to_categorical\n",
+ "from keras.layers import Dense, Conv2D,Flatten,MaxPooling2D,Dropout\n",
+ "from sklearn.svm import NuSVC\n",
+ "from sklearn.svm import SVC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y = []\n",
+ "X = []; \n",
+ "yp = []; \n",
+ "new_X = []\n",
+ "new_X_mel =[]\n",
+ "yp_mel=[]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = 'C:/Users/clyde/Documents/Thinkful/Pre Data Science Bootcamp/Sound Classification Datbase/Train/'\n",
+ "df = pd.read_csv('C:/Users/clyde/Documents/Thinkful/Pre Data Science Bootcamp/Sound Classification Datbase/train.csv')\n",
+ "class_label = df.Class.unique().tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "i=1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "[y,sr] = sf.read('C:/Users/clyde/Documents/Thinkful/Pre Data Science Bootcamp/Sound Classification Datbase/Train/%d.wav'%i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "zz = librosa.feature.melspectrogram(y=y, sr=sr,S=None, n_fft=2048, hop_length=1510)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "S = librosa.feature.inverse.mel_to_stft(zz)\n",
+ "y = librosa.griffinlim(S)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import IPython.display as ipd\n",
+ "ipd.Audio(y,rate=sr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([15809.936 , 15787.592 , 15768.905 , ..., 1401.3564, 1418.318 ,\n",
+ " 1432.3749], dtype=float32)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "librosa.feature.inverse.mfcc_to_audio(zz, n_mels=128, dct_type=2, norm='ortho', ref=1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "q = librosa.istft(S)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ipd.Audio(q,rate=sr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "zzz=librosa.feature.inverse.mel_to_audio(zz, sr=sr, n_fft=2048, hop_length=1510)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ipd.Audio(zzz,rate=sr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[y,sr] = sf.read('C:/Users/clyde/Documents/Thinkful/Pre Data Science Bootcamp/Sound Classification Datbase/Train/%d.wav'%3)\n",
+ "ipd.Audio(y[:,1],rate=sr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.00198364, -0.00192261, -0.00210571, ..., 0.01861572,\n",
+ " 0.15274048, 0.12020874])"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y[:,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 107,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import IPython.display as ipd\n",
+ "n = len(y[0:89999])\n",
+ "hl = 353\n",
+ "n_fft = 511\n",
+ "y_pad = librosa.util.fix_length(y[:,1], n + n_fft // 2)\n",
+ "D = librosa.stft(y_pad, n_fft=n_fft,hop_length=hl)\n",
+ "y_out = librosa.istft(D,length=n,hop_length=hl)\n",
+ "ipd.Audio(y_out,rate=sr)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((256, 256), 89999)"
+ ]
+ },
+ "execution_count": 108,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "D.shape,n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.03317261, 0.08050537, 0.0947876 , ..., -0.01226807,\n",
+ " -0.01998901, -0.03671265])"
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_pad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\clyde\\Anaconda3\\envs\\dask-scipy\\lib\\site-packages\\librosa\\display.py:709: UserWarning: Trying to display complex-valued input. Showing magnitude instead.\n",
+ " warnings.warn('Trying to display complex-valued input. '\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Linear-frequency power spectrogram')"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ "