user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Universal Music Translation Network

Paper content:
We present a method for translating music across musical instruments and styles.
This method is based on unsupervised training of a multi-domain wavenet autoencoder, with a shared encoder and a domain-independent latent space that is trained end-to-end on waveforms.
Employing a diverse training dataset and large net capacity, the single encoder allows us to translate also from musical domains that were not seen during training.
We evaluate our method on a dataset collected from professional musicians, and achieve convincing translations.
We also study the properties of the obtained translation and demonstrate translating even from a whistle, potentially enabling the creation of instrumental music by untrained humans.
Humans have always created music and replicated it -whether it is by singing, whistling, clapping, or, after some training, playing improvised or standard musical instruments.
This ability is not unique to us, and there are many other vocal mimicking species that are able to repeat music from hearing.
Music is also one of the first domains to be digitized and processed by modern computers and algorithms.
It is, therefore, somewhat surprising that in the core music task of mimicry, AI is still much inferior to biological systems.In this work, we present a novel way to produce convincing musical translation between instruments and styles.
For example 1 , we convert the audio of a Mozart symphony performed by an orchestra to an audio in the style of a pianist playing Beethoven.
Our ability builds upon two technologies that have recently become available:
(i) the ability to synthesize high quality audio using autoregressive models, and
(ii) the recent advent of methods that transform between domains in an unsupervised way.
The first technology allows us to generate high quality and realistic audio and thanks to the teacher forcing technique, autoregressive models are efficiently trained as decoders.
The second family of technologies contributes to the practicality of the solution, since posing the learning problem in the supervised setting, would require a parallel dataset of different musical instruments.In our architecture, we employ a single, universal, encoder and apply it to all inputs (universal here means that a single encoder can address all input music, allowing us to achieve capabilities that are known as universal translation).
In addition to the advantage of training fewer networks, this also enables us to convert from musical domains that were not heard during training to any of the domains encountered.The key to being able to train a single encoder architecture, is making sure that the domain-specific information is not encoded.
We do this using a domain confusion network that provides an adversarial signal to the encoder.
In addition, it is important for the encoder not to memorize the input signal but to encode it in a semantic way.
We achieve this by distorting the input audio by random local pitch modulation.
During training, the network is trained as a denoising autoencoder, which recovers the undistorted version of the original input.
Since the distorted input is no longer in the musical domain of the output, the network learns to project out-of-domain inputs to the desired output domain.
In addition, the network no longer benefits from memorizing the input signal and employs a higher-level encoding.Asked to convert one musical instrument to another, our network shows a level of performance that seems to approach that of musicians.
When controlling for audio quality, which is still lower for generated music, it is many times hard to tell which is the original audio file and which is the output of the conversion that mimics a completely different instrument.
The network is also able to successfully process unseen musical instruments such as drums, or other sources, such as whistles.
Our work demonstrates capabilities in music conversion, which is a high-level task (a terminology that means that they are more semantic than low-level audio processing tasks), and could open the door to other high-level tasks, such as composition.
We have initial results that we find interesting: by reducing the size of the latent space, the decoders become more "creative" and produce outputs that are natural yet novel, in the sense that the exact association with the original input is lost.

assistant: text: An automatic method for converting music between instruments and styles