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08/04/2011 · Modulation Synthesis Introduction

Signal Sounds Synthesis Technology E352 Cloud Terrarium Eurorack Oscillator Module - PRE-ORDER Glasgow

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One way we can control signals within a synthesizer is by using a module called an . When an envelope generator receives an "on" gate signal, it sends out a new signal that can be used to control another module. Unlike an oscillator, which repeats its signal over and over again, an envelope generator sends out its signal only once. Like an oscillator, we can look at the signal produced by an envelope generator on a time domain graph:

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Now that we understand oscillators, let's draw a diagram of a very simple synthesizer. This synthesizer will contain a single sawtooth oscillator which sends signal to our audio output, and then to our speakers. The pitch of the oscillator will be controlled by a keyboard.

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Although the algorithm has failed on original signals, the test case with synthetic signals has shown that the form of original signals can be restored.

Let's add an LFO to our synthesizer that causes the pitch of our oscillator to wiggle up and down a little bit, like a violinist moves their hand to create vibrato. We're also going to use the envelope generator to modulate the frequency of our filter, so we get a cool sweeping effect automatically on every note, especially if we turn up the filter's resonance. Using a control signal to change the frequency of another module is called , or , as indicated in the diagram below:

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We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two

Envelopes like the one pictured here are called , so named for their four stages: and . When we put an ADSR envelope module in a synthesizer, we specify exactly what is to happen during each stage of the envelope after an "on" gate signal is received. For example, the envelope pictured above has an attack stage that lasts 250 milliseconds, where the level increases to 1. After that, it has a decay stage lasting 200 milliseconds where the level decreases to 0.7. During the sustain stage, the level stays at 0.7 for as long as the envelope generator is receiving an "on" gate signal. Sustain stages do not have a specified duration. When the gate signal changes to "off," we enter the release stage, where the level takes 200 milliseconds to drop to 0.

Another type of module frequently used to control other modules is the , or . An LFO is just like a normal oscillator, it can have any waveform and amplitude we specify, but it has a very low, sub-audio frequency, producing a very slowly oscillating signal generally used to control other modules within a synthesizer. For example, an LFO might move the volume level of a VCA up and down, creating a tremolo effect. LFOs are like little robots that turn knobs back and forth for you.

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Speech Synthesis Markup Language (SSML) Version 1.1

Individual synthesizer components which perform a single, simple function—such as oscillators and filters—are called . A is a synthesizer made by linking together lots of small modules in interesting ways. In the diagrams we use, the lines connecting the modules are like virtual cables, sending signal between them in much the same way an audio cable would in real life.

The W3C specifications used by VXML to provide speech synthesis.

Sounds are pressure waves which travel through air, or another medium, to our ears. Unlike waves in the ocean, which move up and down, pressure waves move forward and back. These waves move our ear drums in and out, and we experience this as sound. Sound synthesis is the art of creating signals that, when turned into sound waves by a speaker, people find interesting. During the course of this article we'll explore a number of devices that create and modify signals used to synthesize sound.

PixelNN – Example-Based Image Synthesis

We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem. (2) they are not interpretable, making it difficult to control the synthesized output. We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets. We design a simple pipeline that combines the best of both worlds: the first stage uses a convolutional neural network (CNN) to maps the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner. We demonstrate our approach for various input modalities, and for various domains ranging from human faces to cats-and-dogs to shoes and handbags.

PixelNN: Example-based Image Synthesis

There are some problems with our synthesizer design—not the least of which is that because we have no way of controlling the volume of the oscillator, our instrument is always making sound! In order to fix this problem, we need to add a module called a Voltage Controlled Amplifier, or VCA. The function of a VCA is to raise or lower the volume, often called or , of a signal. Essentially, a VCA is a volume knob. Oscillators and other sound generating modules are always making sound, and VCAs are what keep the level down when you're not playing.

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