0xfe

### Keybase proof

I hereby claim:

  * I am 0xfe on github.
  * I am qubit (https://keybase.io/qubit) on keybase.
  * I have a public key ASD0Ru2w_nyBeID3S3RPL2faUhfcmqYupCNqychcsEb2dAo

To claim this, I am signing this object:

0xfe

Verifying my Blockstack ID is secured with the address 15Gn9qP2zjEJcKKxmtfCq232HP9H2RAohK https://explorer.blockstack.org/address/15Gn9qP2zjEJcKKxmtfCq232HP9H2RAohK

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button.quid-pay-button {
    background: rgba(255, 255, 255, 0.1) !important;
    border: none !important;
    border-bottom: 2px #00625d solid !important;
    border-radius: 3px !important;
    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
    font-size: 12px !important;
    margin: 0 !important;
    outline: 0 !important;
    padding: 8px !important;

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GM_PATCHES = [
    0,  # Acoustic Grand Piano
    3,  # Honky-tonk Piano
    6,  # Harpsichord
    11,  # Vibraphone
    16,  # Drawbar Organ
    19,  # Church Organ
    22,  # Harmonica
    24,  # Acoustic Guitar (nylon)
    26,  # Electric Guitar (jazz)

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class Note:
    values = {
        "c": 0,
        "d": 2,
        "e": 4,
        "f": 5,
        "g": 7,
        "a": 9,
        "b": 11
    }

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DefaultConfig = Config(
    rows=513,
    cols = 90,
    s_nperseg = 256,
    s_nfft = 1024,
    s_noverlap = 200,
    resample = 16000)

def spectrogram(file, config=DefaultConfig):
  fs, data = wavfile.read(file)

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self.model = tf.keras.models.Sequential([
        # Add features/channels dim for Conv2D layer
        layers.Reshape((self.s_rows, self.s_cols, 1), input_shape=(self.s_rows, self.s_cols)),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
        tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
        tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(pool_size=(2,2)),

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#!/usr/bin/env python3

import os
import re
import tempfile
import math
import random
import numpy as np
from midiutil.MidiFile import MIDIFile
from midi import Note, GM_PATCHES

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for octave in range(2, 8):
    random.shuffle(GM_PATCHES)
    for patch in range(0, 15):
        program = GM_PATCHES[patch]
        for key in Note.names:
            sample = Sample("samples/note-%s%s-P%s" % (key, octave, program),
                            program=program, key=key, octave=octave)
            sample.make_wav()
            for pitch_shift_hz in np.concatenate((np.array([0]), np.random.randint(10, 80, 5))):
                sample.transform_wav(

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self.model.fit(training_xs, training_ys, epochs=150, batch_size=32,
               callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_mse', patience=25)],
               validation_data=(testing_xs, testing_ys))