Commit ecddf3fc authored by Steve Tjoa's avatar Steve Tjoa

small edits: axis labels, comments, etc.

parent 0298b3b4
{
"metadata": {
"name": "",
"signature": "sha256:43a13ceb38a9caf31a84fb8d9855ad2161713795774983d6f6d7e74fbd66c727"
"signature": "sha256:9733e855fe9a1da482edf975c18e81dce647d9d28b2f7896a11c72c436817884"
},
"nbformat": 3,
"nbformat_minor": 0,
......@@ -42,7 +42,7 @@
]
}
],
"prompt_number": 6
"prompt_number": 1
},
{
"cell_type": "code",
......@@ -58,9 +58,9 @@
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"prompt_number": 2,
"text": [
"<matplotlib.text.Text at 0x4181290>"
"<matplotlib.text.Text at 0x3f49450>"
]
},
{
......@@ -68,11 +68,11 @@
"output_type": "display_data",
"png": 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P7hIFLuivyezZs7F27VoMGzYM69atw+zZswEAhw8fRkFBAQCgtrYW2dnZSE9P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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x411f910>"
"<matplotlib.figure.Figure at 0x3f3df50>"
]
}
],
"prompt_number": 7
"prompt_number": 2
},
{
"cell_type": "code",
......@@ -95,13 +95,13 @@
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"prompt_number": 3,
"text": [
"<IPython.lib.display.Audio at 0x411d810>"
"<IPython.lib.display.Audio at 0x3e4b710>"
]
}
],
"prompt_number": 8
"prompt_number": 3
},
{
"cell_type": "heading",
......@@ -111,6 +111,41 @@
"Windowing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before extracting features from a frame of audio, we first multiply the frame by a window, such as the Hamming window pictured below, to reduce artifacts caused by the edges of the frame."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(hamming(101))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[<matplotlib.lines.Line2D at 0x569e510>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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dgQ4dgMhI2WkEFvhKuHULeO01cSrT4sWcdyeikv73P/Hu/rPPgDfflJ2G/eAr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\n",
"text": [
"<matplotlib.figure.Figure at 0x4426990>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
......@@ -119,6 +154,13 @@
"MFCCs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mel-frequency cepstral coefficients (MFCCs) are a set of features that describe the coarse overall shape of a spectrum but not the fine harmonic structure. MFCCs are often used to describe the timbre of a musical signal."
]
},
{
"cell_type": "code",
"collapsed": false,
......@@ -130,7 +172,32 @@
"\n",
"mfccs = array([mfcc(spectrum(hamming_window(frame)))[1]\n",
" for frame in FrameGenerator(x, frameSize=1024, hopSize=512)])\n",
"\n",
"print mfccs.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(260, 13)\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display MFCCs over time:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"imshow(mfccs[:,1:].T, origin='lower', aspect='auto', interpolation='nearest') # Ignore the 0th MFCC\n",
"yticks(range(12), range(1,13)) # Ignore the 0th MFCC\n",
"ylabel('MFCC Coefficient Index')\n",
......@@ -142,9 +209,9 @@
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"prompt_number": 6,
"text": [
"<matplotlib.text.Text at 0x63b1b90>"
"<matplotlib.text.Text at 0x569ecd0>"
]
},
{
......@@ -152,20 +219,27 @@
"output_type": "display_data",
"png": 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m0Ucf9f1fwXFtLYclzuvCUsgqPaU+RUSiTrjj/8LhjTfeoF+/fhQVFbF69erg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\nyIuIRDEN8iIiUUyDvIhIFNMgLyISxf4/U1M+MF9LxvYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x6615050>"
"<matplotlib.figure.Figure at 0x567c210>"
]
}
],
"prompt_number": 16
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 3,
"level": 2,
"metadata": {},
"source": [
"Centroid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Essentia, `centroid` can be used to compute either the temporal or spectral centroid of a frame."
]
},
{
"cell_type": "code",
"collapsed": false,
......@@ -179,34 +253,30 @@
" for frame in FrameGenerator(x, frameSize=2048, hopSize=1024)])\n",
"\n",
"plot(energy)\n",
"ylabel('MFCC Coefficient Index')\n",
"ylabel('Spectral Centroid')\n",
"xlabel('Frame Index')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'x' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-1b09dbaf5c19>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m energy = array([centroid(spectrum(hamming_window(frame)))\n\u001b[1;32m----> 7\u001b[1;33m for frame in FrameGenerator(x, frameSize=2048, hopSize=1024)])\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menergy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.text.Text at 0x56b2050>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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u3aCpSY/9yO2ChCPs9kQOHTrEzJkzGTx4MEOGDOH666/nq6++8kTbuhyjy1kA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\nQjhNQkQIIYTT/j9W9+55Vr80nQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x54b7650>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k"
],
"language": "python",
"metadata": {},
"outputs": []
"prompt_number": 7
}
],
"metadata": {}
......
{
"metadata": {
"name": "",
"signature": "sha256:3a9c5c69ab347d4cd1921c96b83afd9e14e05093da21ee1ce00532f92c990b15"
"signature": "sha256:0dd68ec8f31142d6f2099173c56528c781516b3e957e1ae1d81a60dce842d246"
},
"nbformat": 3,
"nbformat_minor": 0,
......@@ -60,7 +60,7 @@
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.lib.display.Audio at 0x4176910>"
"<IPython.lib.display.Audio at 0x34b9950>"
]
}
],
......@@ -97,7 +97,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's compute the zero crossing rate and spectral centroid for each frame in the audio signal."
"Let's compute the zero crossing rate and energy for each frame in the audio signal."
]
},
{
......@@ -107,12 +107,9 @@
"from essentia.standard import ZeroCrossingRate, Centroid, Spectrum, Windowing, FrameGenerator, Energy\n",
"hamming_window = Windowing(type='hamming')\n",
"zcr = ZeroCrossingRate()\n",
"centroid = Centroid()\n",
"spectrum = Spectrum()\n",
"energy = Energy()\n",
"\n",
"def compute_features(frame):\n",
" #return array( [zcr(frame), centroid(spectrum(hamming_window(frame)))] ) # Try this too!\n",
" return array( [zcr(frame), energy(frame)] )\n",
"\n",
"features = array([compute_features(frame) for frame in FrameGenerator(simple_loop, frameSize=1024, hopSize=500)])\n",
......@@ -129,7 +126,107 @@
]
}
],
"prompt_number": 13
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(simple_loop)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[<matplotlib.lines.Line2D at 0x3be9390>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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7lwm3SrSeBBE5Q6uQ8GJPYtas4PRN6hqGBJE+tGqWvRgSXuLkFFgrcEIBUee0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\nkCAiIkMMCSIiMvT/TJK8rEJxlYYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x34b9850>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the zero crossing rate:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(features[:,0])\n",
"ylabel('Zero Crossing Rate')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"<matplotlib.text.Text at 0x420b510>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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MHCq7fBkYNMizbfj7i+2uGzfkiUmPPB3fAMTE0doqTzxEWsbEoTI5EgfAdpWn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\n",
"text": [
"<matplotlib.figure.Figure at 0x3bc7150>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the energy:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(features[:,1])\n",
"ylabel('Energy')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.text.Text at 0x4a9eed0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x4228590>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
......@@ -170,7 +267,7 @@
]
}
],
"prompt_number": 14
"prompt_number": 7
},
{
"cell_type": "markdown",
......@@ -193,9 +290,9 @@
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"prompt_number": 8,
"text": [
"<matplotlib.text.Text at 0x5b6ac10>"
"<matplotlib.text.Text at 0x52cead0>"
]
},
{
......@@ -203,11 +300,11 @@
"output_type": "display_data",
"png": 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H/Xn33f/YOUJVFmnSKONefnkyq1fvARYA/wFO4uvryfz5c+wcmaoI2rVrx9mz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\nHDC6AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x565a950>"
"<matplotlib.figure.Figure at 0x4aa1190>"
]
}
],
"prompt_number": 16
"prompt_number": 8
},
{
"cell_type": "heading",
......@@ -252,7 +349,7 @@
]
}
],
"prompt_number": 17
"prompt_number": 9
},
{
"cell_type": "markdown",
......@@ -277,9 +374,9 @@
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"prompt_number": 10,
"text": [
"<matplotlib.text.Text at 0x6bad790>"
"<matplotlib.text.Text at 0x58bb7d0>"
]
},
{
......@@ -287,11 +384,11 @@
"output_type": "display_data",
"png": 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3fGliAg2A2wBWm5vDl1ezVAImDT335NLoAHRLmRNVlsXLluGajw/qGRvD3sgI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\nVHWYRES1UkVO/1V2pREYGIi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"text": [
"<matplotlib.figure.Figure at 0x6bb64d0>"
"<matplotlib.figure.Figure at 0x58b28d0>"
]
}
],
"prompt_number": 26
"prompt_number": 10
}
],
"metadata": {}
......
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