AudioMelody
Professional-grade AI Harmonic Synthesis and Stem Reconstruction for Modern Sound Engineering.

Neural sound synthesis through deep learning-based instrument interpolation and latent space exploration.
NSynth Super is an experimental hardware instrument and software ecosystem developed by Google Creative Lab and the Magenta team. It utilizes the NSynth (Neural Synthesizer) algorithm, a deep learning model that uses a WaveNet-style autoencoder to learn the core characteristics of over 100,000 recorded sounds. Unlike traditional synthesizers that layer or filter audio, NSynth Super performs mathematical interpolation within the latent space of sounds to create entirely new timbres that possess the acoustic qualities of both source instruments simultaneously. Architecturally, the system is designed to run on a Raspberry Pi integrated with a custom PCB and a 4x4 touch interface, allowing users to navigate a grid of four distinct instrument types. As we move into 2026, NSynth Super remains a foundational reference for neural audio synthesis, offering musicians and sound designers a tactile way to explore the 'mathematical middle' of instruments—such as a 'flute-marimba' or a 'cello-glass' hybrid. The project is fully open-sourced, providing the community with the hardware schematics, firmware, and pre-trained neural models necessary to build and extend the technology.
Uses a temporal convolutional neural network to encode raw audio into 16-dimensional vectors for processing.
Professional-grade AI Harmonic Synthesis and Stem Reconstruction for Modern Sound Engineering.
Transform static PDFs and long-form documents into immersive, studio-quality audiobooks using neural TTS.
The premier generative audio platform for lifelike speech synthesis and voice cloning.
Enterprise-grade AI music composition for instant, royalty-free creative workflows.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Performs linear interpolation between the mathematical representations of four different instrument sounds.
Optimized C++ kernels for running neural inference on ARM-based hardware with low latency.
Ability to train the model on user-provided audio datasets using the Magenta Python library.
Assigns four distinct sound 'anchors' to corners of a touch interface for spatial sound blending.
Creating unique, 'otherworldly' textures that retain the organic feel of real instruments.
Registry Updated:2/7/2026
Smoothly transitioning between distinct lead sounds during a live set without jarring cuts.
Generating thousands of unique one-shot samples for commercial distribution.