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Google Open NSynth Super

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Description
NSynth Super is part of an ongoing experiment by Magenta: a research project within Google that explores how machine learning tools can help artists create art and music in new ways.

Technology has always played a role in creating new types of sounds that inspire musicians—from the sounds of distortion to the electronic sounds of synths. Today, advances in machine learning and neural networks have opened up new possibilities for sound generation.

Building upon past research in this field, Magenta created NSynth (Neural Synthesizer). It’s a machine learning algorithm that uses a deep neural network to learn the characteristics of sounds, and then create a completely new sound based on these characteristics.

Rather than combining or blending the sounds, NSynth synthesizes an entirely new sound using the acoustic qualities of the original sounds—so you could get a sound that’s part flute and part sitar all at once.

Since the release of NSynth, Magenta have continued to experiment with different musical interfaces and tools to make the output of the NSynth algorithm more easily accessible and playable.

As part of this exploration, they've created NSynth Super in collaboration with Google Creative Lab. It’s an open source experimental instrument which gives musicians the ability to make music using completely new sounds generated by the NSynth algorithm from 4 different source sounds. The experience prototype (pictured above) was shared with a small community of musicians to better understand how they might use it in their creative process.

NSynth uses deep neural networks to generate sounds at the level of individual samples. Learning directly from data, NSynth provides artists with intuitive control over timbre and dynamics, and the ability to explore new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer.

NSynth is an algorithm that can generate new sounds by combining the features of existing sounds. To do that, the algorithm takes different sounds as input.

Using an autoencoder, it extracts 16 defining temporal features from each input. These features are then interpolated linearly to create new embeddings (mathematical representations of each sound). These new embeddings are then decoded into new sounds, which have the acoustic qualities of both inputs.

A full description can be found on the Magenta blog. The dataset and algorithm can be found in the research paper Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders on the Google Research page.

All of the technology and design used to create NSynth Super is available as an open source project

Like all Magenta projects, NSynth Super is built using open source libraries, including TensorFlow and openFrameworks, to enable a wider community of artists, coders, and researchers to experiment with machine learning in their creative process.

The open source version of the NSynth Super prototype including all of the source code, schematics, and design templates are available for download on GitHub.
Images

Videos
Technical Specifications
Type: Digital
Synthesis: Resynthesis, Sampling, Vector synthesis, Wave Table
Oscillators
Oscillators: 4
Waveforms: ROM, Wave Table
Osc Modulation: Envelope, Input
Oscillator Notes:
4 sound sources
16 sound model elements
Envelopes
Envelopes: 1
Evelope Paramerters: Attack, Decay, Decay 2, Sustain, Release
Polyphony & Tuning
Polyphony: 16
Timbrality: 1
Tuning: Atonal, Micro, Standard
Modes: Polyphonic
Storage: Internal, USB
Editing: USB
Case
Case: Desktop
Controls: Knobs
Display Type: LED, OLED
Dimensions (WxDxH): 160mm x 160mm x 30mm
Connections
Audio Output Connections: 1/8" Phone Jack, Stereo Main
MIDI Ports: USB
DAC Bits: 16
DAC Frequency Rate: 44.1
Power: 5V USB 2+ amps
Production
Year Released: 2018

Product Links
Company Product Sites:
[+] nsynthsuper.withgoogle.com
Pricing
MSRP List Price: Open Source - convert
Shopping
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References & Sources

  Report Synthesizer
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