Category

ML

Waybackprop

By | machinelearning, ML, TensorFlow

Waybackprop

TL;DR

I review (with animations!) backprop and truncated backprop through time (TBPTT), and introduce a multi-scale adaptation of TBPTT to hierarchical recurrent neural networks that has logarithmic space complexity. I wished to use this to study long-term dependencies, but the implementation got too complicated and kind of collapsed under its own weight. Finally, I lay out some reasons why long-term dependencies are difficult to deal with, going above and beyond the well-studied sort of gradient vanishing that is due to system dynamics.

Introduction

Last summer at Magenta, I took on a somewhat ambitious project. Whereas most of Magenta was working on the symbolic level (scores, MIDI, pianorolls), I felt that this left out several important aspects of music, such as timbre and phrasing. Instead, I decided to work on a generative model of real audio.

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Generate your own sounds with NSynth

By | machinelearning, ML, TensorFlow

[Editorial Note: One of the best parts of working on the Magenta project is getting to interact with the awesome community of artists and coders. Today, we’re very happy to have a guest blog post by one of those community members, Parag Mital, who has implemented a fast sampler for NSynth to make it easier for everyone to generate their own sounds with the model.]

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Making a Neural Synthesizer Instrument

By | machinelearning, ML, TensorFlow

In a previous post, we described the details of NSynth (Neural Audio Synthesis), a new approach to audio synthesis using neural networks. We hinted at further releases to enable you to make your own music with these technologies. Today, we’re excited to follow through on that promise by releasing a playable set of neural synthesizer instruments:

  • An interactive AI Experiment made in collaboration with Google Creative Lab that lets you interpolate between pairs of instruments to create new sounds.

Generating Long-Term Structure in Songs and Stories

By | machinelearning, ML, TensorFlow

One of the difficult problems in using machine learning to generate sequences, such as melodies, is creating long-term structure. Long-term structure comes very naturally to people, but it’s very hard for machines. Basic machine learning systems can generate a short melody that stays in key, but they have trouble generating a longer melody that follows a chord progression, or follows a multi-bar song structure of verses and choruses. Likewise, they can produce a screenplay with grammatically correct sentences, but not one with a compelling plot line. Without long-term structure, the content produced by recurrent neural networks (RNNs) often seems wandering and random.

But what if these RNN models could recognize and reproduce longer-term structure? Read More

Magenta MIDI Interface

By | machinelearning, ML, TensorFlow

The magenta team is happy to announce our first step toward providing an easy-to-use
interface between musicians and TensorFlow. This release makes it
possible to connect a TensorFlow model to a MIDI controller and synthesizer in
real time.

Don’t have your own MIDI keyboard? There are many free software
components you can download and use with our interface. Find out more details on
setting up your own TensorFlow-powered MIDI rig in the
README.

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Multistyle Pastiche Generator

By | machinelearning, ML, TensorFlow

Vincent Dumoulin,
Jonathon Shlens,
and
Manjunath Kudlur
have extended image style transfer by creating a single network which performs
more than one stylization of an image. The
paper[1] has also been summarized
in a
Google
Research Blog
post. The source code and trained models behind the paper
are being released here.

The model creates a succinct description of a style. These descriptions can be
combined to create new mixtures of styles. Below is a picture of Picabo[5] stylized with a mixture of 3 different styles. Adjust the sliders below the
image to create more styles.

30%

35%

35%

var pasticheDemo = function(img_id, url_prefix) {

function getValue(index) {
return parseFloat(document.getElementById(img_id + ‘_style_’ + index).value);
}

function normalizeValues(values) {
var sum = values[0] + values[1] + values[2];
if (sum <= 0) {
return [0, 0, 0];
}
var normValue = function(v) {
return Math.round(v * 20 / sum) * 50;
};
var norm = [
normValue(values[0]), normValue(values[1]), normValue(values[2])];
sum = norm[0] + norm[1] + norm[2];
var diff = 1000 – sum;
var max = Math.max(norm[0], norm[1], norm[2]);
if (norm[0] == max) {
norm[0] += diff;
} else if (norm[1] == max) {
norm[1] += diff;
} else {
norm[2] += diff;
}
return norm;
}

function imageHash(values) {
var toString = function(v) {
var str = String(v);
while (str.length < 3) {
str = '0' + str;
}
// AdBlock Plus looks for patterns that match common ad image sizes.
// Breaking up the number with a character is enough to bypass this.
str = str[0] + 'a' + str.substr(1);
return str;
};
return toString(values[0]) + '_' + toString(values[1]) + '_' +
toString(values[2]);
}

function getImageUrl(prefix, values) {
href = '/assets/style_blends/' + prefix + '_' + imageHash(values) + '.jpg';
return href;
}

var preloadedImages = null;

function createImage(values) {
var img = new Image();
img.id = img_id;
var contents = {
'isloaded': false,
'image': img
};
img.onload = function() {
contents.isloaded = true;
};
img.src = getImageUrl(url_prefix, values);
return contents;
}

function getImage(values) {
var hash = imageHash(values);
var contents = preloadedImages[hash];
if (contents.isloaded) {
return contents.image;
} else {
preloadedImages[hash] = createImage(values);
}
return contents.image;
}

function loadAllImages() {
var images = {};
for (var x = 0; x <= 1000; x += 50) {
for (var y = 0; y <= 1000 – x; y += 50) {
for (var z = 0; z <= 1000 – x – y; z += 50) {
if (x + y + z == 1000 || x + y + z == 0) {
images[imageHash([x, y, z])] = createImage([x, y, z]);
}
}
}
}
return images;
}

function displayImage(image) {
var current = document.getElementById(img_id);
// Load the new image with the height of the current image so the slider
// stays in the same place.
image.width = current.width;
image.height = current.height;
var parent = current.parentElement;
parent.removeChild(current);
parent.appendChild(image);
}

function setWeightLabels(values) {
for (var index = 0; index < 3; ++index) {
var weight = document.getElementById(img_id + '_weight_' + index);
weight.innerHTML = (values[index] / 10) + '%';
}
}

function sliderChange() {
if (preloadedImages == null) {
preloadedImages = loadAllImages();
}

var img = document.getElementById(img_id);
var values = [getValue(0), getValue(1), getValue(2)];
var normalized = normalizeValues(values);
displayImage(getImage(normalized));
setWeightLabels(normalized);
}

document.getElementById(img_id + '_style_0').oninput = sliderChange;
document.getElementById(img_id + '_style_1').oninput = sliderChange;
document.getElementById(img_id + '_style_2').oninput = sliderChange;

};
pasticheDemo('picabo_deck', 'picabo');

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