faroit

from __future__ import division
import numpy as np
import essentia.standard
import matplotlib.pyplot as plt


def sinewave(f0, nseconds, samplerate=44100):
    t = np.arange(1, samplerate * nseconds)
    sig = np.zeros_like(t)
    return sig + 0.5 * np.sin(2 * np.pi * f0 * t / samplerate)

faroit

from git import Repo  # requires gitpython

repo_dir = os.path.abspath(os.path.dirname(__file__))
commit = Repo(repo_dir).head.commit.hexsha[:8]

faroit

# -*- coding: utf-8 -*-
"""
Bandlimited versions of scipy.signal.waveforms.

Intent is mathematical perfection over performance;
these use additive synthesis, so they are slow, but exact.

Less ideal methods using BLIT:
Sawtooth can be made by integrating BLIT minus a DC value to prevent integrator wandering off
Square can be made by integrating bipolar BLIT

faroit

# $ brew install https://raw.github.com/gist/1121425/rubberband.rb

require 'formula'

class Rubberband < Formula
  homepage ''
  url 'http://code.breakfastquay.com/attachments/download/35/rubberband-1.8.1-gpl-executable-osx-universal.tar.bz2'
  version '1.8.1'
  sha1 'bceba7dba8cdd773a318ca973ed9c5806be03262'

faroit

import pydsd

def my_function(dsd_track):
    # do your fancy bss algorithm

    # use the tracks mixture audio as numpy array
    dsd_track.audio

    # get the path for external processing
    dsd_track.path

faroit

# Define the Input sources for each track
sources:
    vocals: vocals.wav
    drums: drums.wav
    bass: bass.wav
    other: other.wav

# Additionally provide the artistic mix
mix:
    mixture.wav

faroit

PYTHON

import numpy as np
import timeit

P = np.random.random((20, 15, 100, 30, 2))

A = np.random.random((20, 15, 100, 5))
H = np.random.random((30, 5))
C = np.random.random((2, 5))

faroit

USER=your_github_username

# Create a token via commandline
# curl -u "$USER" -d '{"scopes":["repo"],"note":"migrate.sh"}' https://api.github.com/authorizations

TOKEN=yourtoken
REPONAME=${PWD##*/}

# Create repository and return the git url
URL=$( curl -v -XPOST -H "Authorization: token $TOKEN" https://api.github.com/user/repos \

faroit

from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments
        output_dim: dimension of the internal projections and the final output.

faroit

Keybase proof

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• I am faroit on github.
• I am faroit (https://keybase.io/faroit) on keybase.
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To claim this, I am signing this object: