backup

++Subproject commit 7d8844740c119ae66576be9510474a791240a745

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.fftpack import fft\n",
+    "from scipy.io import wavfile # get the api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'numpy.int16' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-c176e6e452f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwavfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1.wav'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# load the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# this is a two channel soundtrack, I get the first track\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mele\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m8.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mele\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# this is 8-bit track, b is now normalized on [-1,1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# calculate fourier transform (complex numbers list)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# you only need half of the fft list (real signal symmetry)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'numpy.int16' object is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "fs, data = wavfile.read('1.wav') # load the data\n",
+    "a = data.T[0] # this is a two channel soundtrack, I get the first track\n",
+    "b=[(ele/2**8.)*2-1 for ele in a] # this is 8-bit track, b is now normalized on [-1,1)\n",
+    "c = fft(b) # calculate fourier transform (complex numbers list)\n",
+    "d = int(len(c)/2)  # you only need half of the fft list (real signal symmetry)\n",
+    "plt.plot(abs(c[:(d-1)]),'r') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#wav파일 프린트 할줄알아야함 \n",
+    "#(채널 : 모노라면 1, 스테레오라면 2\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import wave\n",
+    "import sys\n",
+    "import struct\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: '/run/user/1000/jupyter/kernel-6454a929-4509-4b51-949d-f1c910f7ce09.json'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-cc8beb2556b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msample_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetframerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtotal_num_samps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetnframes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfft_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mnum_fft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtotal_num_samps\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfft_length\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '/run/user/1000/jupyter/kernel-6454a929-4509-4b51-949d-f1c910f7ce09.json'"
+     ]
+    }
+   ],
+   "source": [
+    "# 웨이브 파일을 열어라\n",
+    "fp = wave.open('birdsound.wav',\"rb\")\n",
+    "sample_rate = fp.getframerate()\n",
+    "total_num_samps = fp.getnframes()\n",
+    "fft_length = int(sys.argv[2])\n",
+    "num_fft = (total_num_samps / fft_length ) - 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 임시로 사용할 작업 배열을 만들어라\n",
+    "temp = zeros((num_fft,fft_length),Float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 파일로부터 데이터를 읽어 들여라\n",
+    "for i in range(num_fft):\n",
+    "    tempb = fp.readframes(fft_length);\n",
+    "    temp[i,:] = array(struct.unpack(\"%dB\"%(fft_length), \\\n",
+    "                tempb),Float) - 128.0\n",
+    "fp.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 데이터를 창틀화하라\n",
+    "temp = temp * hamming(fft_length)\n",
+    "\n",
+    "# FFT를 사용하여 변환하라, 파워를 반환하라\n",
+    "freq_pwr  = 10*log10(1e-20+abs(real_fft(temp,fft_length))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 결과를 도표하라\n",
+    "n_out_pts = (fft_length / 2) + 1\n",
+    "y_axis = 0.5*float(sample_rate) / n_out_pts * \\\n",
+    "         arange(n_out_pts)\n",
+    "x_axis = (total_num_samps / float(sample_rate)) / \\\n",
+    "         num_fft * arange(num_fft)\n",
+    "setvar(\"X\",\"Time (sec)\")\n",
+    "setvar(\"Y\",\"Frequency (Hertz)\")\n",
+    "conshade(freq_pwr,x_axis,y_axis)\n",
+    "disfin()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import wave\n",
+    "import pyaudio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def play_file(fname):\n",
+    "    #오디오 객체 생성\n",
+    "    wf = wave.open('output.wav','rb') # wave파일 할당해준 객체\n",
+    "    p = pyaudio.PyAudio() #파이오디오 할당해준 객체\n",
+    "    chunk = 1024\n",
+    "    \n",
+    "    #stream = pyaudio로 open 하는것\n",
+    "    stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),\n",
+    "                    channels=wf.getnchannels(),\n",
+    "                    rate=wf.getframerate(),\n",
+    "                    output=True)\n",
+    "    \n",
+    "    #데이터 wav파일에서 읽기\n",
+    "    data = wf.readframes(chunk)\n",
+    "    \n",
+    "    #읽은 데이터 있는동안 \n",
+    "    while data !='':\n",
+    "        stream.write(data) #스트림에 데이터 쓰기 \n",
+    "        data = wf.readframes(chunk) #데이터 wav파일에서 다시읽기\n",
+    "    \n",
+    "    #객체 닫아주기\n",
+    "    stream.close()\n",
+    "    p.terminate()\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "play_file('output.wav')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    import numpy as np\n",
+    "    import pylab\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    from scipy.io import wavfile\n",
+    "    import time\n",
+    "    import sys\n",
+    "    import seaborn as sns\n",
+    "    import pyaudio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "i=0\n",
+    "f,ax = plt.subplots(2)\n",
+    "\n",
+    "# Prepare the Plotting Environment with random starting values\n",
+    "x = np.arange(10000)\n",
+    "y = np.random.randn(10000)\n",
+    "\n",
+    "# Plot 0 is for raw audio data\n",
+    "li, = ax[0].plot(x, y)\n",
+    "ax[0].set_xlim(0,1000)\n",
+    "ax[0].set_ylim(-5000,5000)\n",
+    "ax[0].set_title(\"Raw Audio Signal\")\n",
+    "# Plot 1 is for the FFT of the audio\n",
+    "li2, = ax[1].plot(x, y)\n",
+    "ax[1].set_xlim(0,5000)\n",
+    "ax[1].set_ylim(-100,100)\n",
+    "ax[1].set_title(\"Fast Fourier Transform\")\n",
+    "# Show the plot, but without blocking updates\n",
+    "plt.pause(0.01)\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "FORMAT = pyaudio.paInt16 # We use 16bit format per sample\n",
+    "CHANNELS = 1\n",
+    "RATE = 44100\n",
+    "CHUNK = 1024 # 1024bytes of data red from a buffer\n",
+    "RECORD_SECONDS = 0.1\n",
+    "WAVE_OUTPUT_FILENAME = \"file.wav\"\n",
+    "\n",
+    "audio = pyaudio.PyAudio()\n",
+    "\n",
+    "# start Recording\n",
+    "stream = audio.open(format=FORMAT,\n",
+    "                    channels=CHANNELS,\n",
+    "                    rate=RATE,\n",
+    "                    input=True)#,\n",
+    "                    #frames_per_buffer=CHUNK)\n",
+    "\n",
+    "global keep_going\n",
+    "keep_going = True\n",
+    "\n",
+    "def plot_data(in_data):\n",
+    "    # get and convert the data to float\n",
+    "    audio_data = np.fromstring(in_data, np.int16)\n",
+    "    # Fast Fourier Transform, 10*log10(abs) is to scale it to dB\n",
+    "    # and make sure it's not imaginary\n",
+    "    dfft = 10.*np.log10(abs(np.fft.rfft(audio_data)))\n",
+    "\n",
+    "    # Force the new data into the plot, but without redrawing axes.\n",
+    "    # If uses plt.draw(), axes are re-drawn every time\n",
+    "    #print audio_data[0:10]\n",
+    "    #print dfft[0:10]\n",
+    "    #print\n",
+    "    li.set_xdata(np.arange(len(audio_data)))\n",
+    "    li.set_ydata(audio_data)\n",
+    "    li2.set_xdata(np.arange(len(dfft))*10.)\n",
+    "    li2.set_ydata(dfft)\n",
+    "\n",
+    "    # Show the updated plot, but without blocking\n",
+    "    plt.pause(0.01)\n",
+    "    if keep_going:\n",
+    "        return True\n",
+    "    else:\n",
+    "        return False\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Open the connection and start streaming the data\n",
+    "stream.start_stream()\n",
+    "print (\"\\n+---------------------------------+\")\n",
+    "print (\"| Press Ctrl+C to Break Recording |\")\n",
+    "print (\"+---------------------------------+\\n\")\n",
+    "\n",
+    "# Loop so program doesn't end while the stream callback's\n",
+    "# itself for new data\n",
+    "while keep_going:\n",
+    "    try:\n",
+    "        plot_data(stream.read(CHUNK))\n",
+    "    except KeyboardInterrupt:\n",
+    "        keep_going=False\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "# Close up shop (currently not used because KeyboardInterrupt\n",
+    "# is the only way to close)\n",
+    "stream.stop_stream()\n",
+    "stream.close()\n",
+    "\n",
+    "audio.terminate()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TEST할것\n",
+    "<입력값>\n",
+    "1. 일정 데시벨파워 이하는 다 지운 멜스펙토그램 <-아... 원래 데이터셋 새소리 어느정도 작은 소리까지 잡는겨\n",
+    "2. mel filter 80 & axis=1평균을 뺀 멜스펙토그램\n",
+    "3. 새소리 주파수 이하는 주파수대는 짜름\n",
+    "5. 커널씩 보면서 차이나는것만 뽑아내기 (그주변만 비교하는게 있을거같은데)\n",
+    "6. 멜필터 안씌운거 보고 새소리 주파수 이하 짤라버릴때등.. 안씌운거 보기\n",
+    "\n",
+    "<모델>\n",
+    "1. 일반 뛰어난 CNN모델. 첫번째 모델이 왜 더 좋은지 보자\n",
+    "3. C+RNN <- RNN 부분 잘 된건가?\n",
+    "4. Capsul network\n",
+    "\n",
+    "<추가적>\n",
+    "1.  라벨링 : 확실히 귀에 잘 들리고 눈에 잘 보이는 애들만 1로 라벨링, 희미한건 0으로 라벨링 \n",
+    "    -> 필드테스트 한 애들만 맞춘거 비율이 높도록 보이기. 머신이 새라고 한거중에 0인애들만 또 보여주기\n",
+    "2. 필드테스트랑 원래하던거랑 왜안될까? -> 짹짹이가 데이터셋에 별로없거나 모델이 안좋거나.. (먼저 컴한테 분류시켜보고 판단?)\n",
+    "3. 그경우, 찌르레기 소리에 초점을 맞춰서 저 패턴을 학습시키고 아예 그걸 찾도록 하는것도 나쁘지 않을듯\n",
+    "4. 아이폰녹음이랑 뭐가다른지, 실제로 차이가 난건지도 봐야함. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. CRNN - RNN 코드 다시 보고 돌리기\n",
+    "2. CNN - 논문1네 모델 돌리기\n",
+    "3. 캡슐 네트워크 돌리기\n",
+    "\n",
+    "#### scipy / librosa 둘다로 mel spectogram 짜봤는데 librosa가 더 좋았음."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyaudio\n",
+    "import wave"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* recording\n",
+      "* done recording\n"
+     ]
+    }
+   ],
+   "source": [
+    "CHUNK = 1024\n",
+    "FORMAT = pyaudio.paInt16\n",
+    "CHANNELS = 1\n",
+    "RATE = 25600\n",
+    "RECORD_SECONDS = 10\n",
+    "WAVE_OUTPUT_FILENAME = \"test.wav\"\n",
+    "\n",
+    "p = pyaudio.PyAudio()\n",
+    "\n",
+    "stream = p.open(format=FORMAT,\n",
+    "                channels=CHANNELS,\n",
+    "                rate=RATE,\n",
+    "                input=True,\n",
+    "                frames_per_buffer=CHUNK)\n",
+    "\n",
+    "print(\"* recording\")\n",
+    "\n",
+    "frames = []\n",
+    "\n",
+    "for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n",
+    "    data = stream.read(CHUNK)\n",
+    "    frames.append(data)\n",
+    "\n",
+    "print(\"* done recording\")\n",
+    "\n",
+    "stream.stop_stream()\n",
+    "stream.close()\n",
+    "p.terminate()\n",
+    "\n",
+    "wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')\n",
+    "wf.setnchannels(CHANNELS)\n",
+    "wf.setsampwidth(p.get_sample_size(FORMAT))\n",
+    "wf.setframerate(RATE)\n",
+    "wf.writeframes(b''.join(frames))\n",
+    "wf.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pydub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pydub import AudioSegment\n",
+    "\n",
+    "for i in range(0,45):\n",
+    "    t1 = i * 10000 #Works in milliseconds\n",
+    "    t2 = (i+1) * 10000\n",
+    "    \n",
+    "    newAudio = AudioSegment.from_wav(\"./New/IMG_3867.wav\")\n",
+    "    newAudio = newAudio[t1:t2]\n",
+    "    newAudio.export('./field/IMG_3867%d.wav'%(i), format=\"wav\")\n",
+    "\n",
+    "print('done')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}