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{ | |
"description": "CQT no beats", | |
"hypers": "{\"objective\": {\"hyper_dims\": {\"44672\": {\"bscale\": [0], \"bcenter\": [0]}, \"44673\": {\"bscale\": [1], \"bcenter\": [1]}, \"44674\": {\"bscale\": [2], \"bcenter\": [2]}, \"44675\": {\"bscale\": [3], \"bcenter\": [3]}, \"44676\": {\"bscale\": [4], \"bcenter\": [4]}, \"44677\": {\"bscale\": [5], \"bcenter\": [5]}, \"44678\": {\"bscale\": [6], \"bcenter\": [6]}}, \"nnet_parameters\": [{\"shape\": [7, 50], \"value\": \"eJwNlvc/FI4fx1FCwuejoRJJoSgpWsI7pOxZyPooojT4IDK+iRZKmRll5M7qzLM53hzHcZxZx4nO\\nqMgKDZ+sb//A65fn6/F8vb6Clzq888Cbknx/Jc+HwvcJPeVgp0k07ai6KUwJhwb3AlXbUTJ6UTaN\\nxUQVodpUv2Ui+SYOVI2mCPmycUNqDWGLJgHeeYYyv0TlQEr2S4N06Vo4Jra2+W79G0D/vrVfke0g\\npfbzzfCBbthy07Z/TO06Kscz6MaxHfiI97vQYXIfuEbUclUwmuDz/QslrtUsrPzVLksVHkS53Z61\\nRtIIe7W6j+jOEeG2uNf9F9JFmE31cNjBRcLFro2+vBJVKPD2qOzsQCOmUJWknlIG8JLLHe0Ht5tx\\nw5NqcpFfNxw3n7yVkYlQWEVQkCguwsHe8ZZDuaNYWnhya2MJwuhcxgaWTgvWRpNi/hlm4by9pg0h\\nmQrHTRP03YRJKK1bcErerQddRzkmPP8lwuUzEjsoLyqxcZ/ckxXjepTjFcyb+smBF2Of6s |
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''' | |
Dynamic time warping implementation in Theano | |
See also | |
https://github.com/astanway/theano-dtw | |
https://github.com/danielrenshaw/TheanoBatchDTW | |
''' | |
import theano | |
import theano.tensor as T |
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import tempfile | |
import subprocess | |
import os | |
import pretty_midi | |
import librosa | |
def fast_fluidsynth(m, fs): | |
''' | |
Faster fluidsynth synthesis using the command-line program | |
instead of pyfluidsynth. |
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import matplotlib.pyplot as plt | |
def draw_neural_net(ax, left, right, bottom, top, layer_sizes): | |
''' | |
Draw a neural network cartoon using matplotilb. | |
:usage: | |
>>> fig = plt.figure(figsize=(12, 12)) | |
>>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2]) | |
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