├── run_jupyter.sh
├── jupyter_notebook_config.py
├── README.md
├── Dockerfile
└── Label data.ipynb


/run_jupyter.sh:
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1 | #!/bin/bash
2 | jupyter notebook "$@" --NotebookApp.token=abc --allow-root
3 | 


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/jupyter_notebook_config.py:
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 1 | import os
 2 | from IPython.lib import passwd
 3 | 
 4 | c.NotebookApp.ip = '*'
 5 | c.NotebookApp.port = int(os.getenv('PORT', 8888))
 6 | c.NotebookApp.open_browser = False
 7 | c.MultiKernelManager.default_kernel_name = 'python2'
 8 | c.NotebookApp.password = '' 
 9 | c.NotebookApp.token = ''
10 | 


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/README.md:
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 1 | # batdetection
 2 | Detecting the sounds bats make in my backyard. 
 3 | 
 4 | To run this code, install Docker. 
 5 | Steps to get it running: 
 6 | `docker run -p 8888:8888 rmeertens/batdetection`
 7 | Then navigate in your browser to localhost:8888
 8 | 
 9 | 
10 | Post that explains what I did: http://www.pinchofintelligence.com/detecting-bats-recognising-sound-tensorflow/
11 | 


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/Dockerfile:
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 1 | FROM tensorflow/tensorflow:latest
 2 | # Various Python and C/build deps
 3 | RUN apt-get update && apt-get install -y \ 
 4 |     wget \
 5 |     build-essential \ 
 6 |     cmake \ 
 7 |     git \
 8 |     unzip \ 
 9 |     pkg-config \
10 |     python-dev \ 
11 |     python-opencv \ 
12 |     libopencv-dev \ 
13 |     libav-tools  \ 
14 |     libjpeg-dev \ 
15 |     libpng-dev \ 
16 |     libtiff-dev \ 
17 |     libjasper-dev \ 
18 |     libgtk2.0-dev \ 
19 |     python-numpy \ 
20 |     python-pycurl \ 
21 |     libatlas-base-dev \
22 |     gfortran \
23 |     webp \ 
24 |     python-opencv \ 
25 |     qt5-default \
26 |     libvtk6-dev \ 
27 |     zlib1g-dev 
28 | 
29 | COPY jupyter_notebook_config.py /root/.jupyter/
30 | COPY run_jupyter.sh /
31 | RUN chmod +x /run_jupyter.sh
32 | RUN pip install Cython --install-option="--no-cython-compile"
33 | RUN pip install tflearn scikit-image scikit-learn pandas keras
34 | RUN pip install librosa
35 | WORKDIR "/notebooks" 
36 | ADD . /notebooks
37 | CMD ["/run_jupyter.sh"]
38 | 


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/Label data.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "# Labeling sound data\n",
 8 |     "Last week I discovered that there are bats outside of my appartment. To detect when there is one I took a \"bat detector\", a device that brings the high pitched noise bats make back to hearable levels. To split the data into \"batsound\" and \"no sound\" I made this Jupyter notebook to label the data.\n",
 9 |     "\n",
10 |     "Note that we use the librosa library to load the data. Humans can label either as bat (1) or no bat (0). Audiosamples of 1 second are saved to the bat and no-bat folders. \n"
11 |    ]
12 |   },
13 |   {
14 |    "cell_type": "code",
15 |    "execution_count": null,
16 |    "metadata": {},
17 |    "outputs": [],
18 |    "source": [
19 |     "import librosa\n",
20 |     "import os\n",
21 |     "import numpy as np\n",
22 |     "import matplotlib.pyplot as plt\n",
23 |     "import IPython\n",
24 |     "\n",
25 |     "\n",
26 |     "directory = \"batsounds\"\n",
27 |     "filename = \"bats8.m4a\"\n",
28 |     "openfile = os.path.join(directory, filename)\n",
29 |     "\n",
30 |     "print(\"Loading \" + openfile)\n",
31 |     "soundarray, sr = librosa.load(openfile) # This operation can take a long time with large sound files\n",
32 |     "\n",
33 |     "# We take 1 second per sample, which comes down to sampling-rate variables per sample. \n",
34 |     "maxseconds = int(len(X)/sr)\n",
35 |     "for second in range(maxseconds-1):\n",
36 |     "    print(str(second) + \" out of \" + str(maxseconds))\n",
37 |     "    audiosample = np.array(soundarray[second*sr:(second+1)*sr])\n",
38 |     "    IPython.display.display(IPython.display.Audio(audiosample, rate=sr,autoplay=True))\n",
39 |     "    label = str(input(\"bat(1) or not (0)\"))\n",
40 |     "\n",
41 |     "    outputfilename = os.path.join(\"labeled\", label, filename + str(second) + \".wav\")\n",
42 |     "    print(\"Saving to: \" + outputfilename)\n",
43 |     "    librosa.output.write_wav(outputfilename, audiosample, sr)\n"
44 |    ]
45 |   }
46 |  ],
47 |  "metadata": {
48 |   "kernelspec": {
49 |    "display_name": "Python 2",
50 |    "language": "python",
51 |    "name": "python2"
52 |   },
53 |   "language_info": {
54 |    "codemirror_mode": {
55 |     "name": "ipython",
56 |     "version": 2
57 |    },
58 |    "file_extension": ".py",
59 |    "mimetype": "text/x-python",
60 |    "name": "python",
61 |    "nbconvert_exporter": "python",
62 |    "pygments_lexer": "ipython2",
63 |    "version": "2.7.12"
64 |   }
65 |  },
66 |  "nbformat": 4,
67 |  "nbformat_minor": 2
68 | }
69 | 


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