Musical AI is fast evolving. In March, Google released an algorithmic Google Doodle that let users create melodic homages to Bach. The MuseNet composer has three modes: simple mode, which plays an uncurated sample from a composer or style and an optional start of a famous pieceand advanced mode, which lets you can interact with the model directly to create a novel piece.

This AI-generated musak shows us the limit of artificial creativity

But uniquely, it has attention: Every output element is connected to every input element, and the weightings between them are calculated dynamically. During training, they were prepended to each music sample so that MuseNet learned to use them information in making note predictions.

And predictably, it has a difficult time with incongruous pairings of styles and instruments, such as Chopin with bass and drums.

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Stuck inside? View all deals.Celebrating Day with Culture, Gyeonggi Museum of Art is launching a special-themed exhibition focusing with historic relics that frequently appear in the history textbooks in order to approach a step closer to the audience in a friendly way. The year is the th anniversary of the March 1st Movement.

InJapan integrated Korea with its nation with force, but the people of Korea did not succumb to this with the hope of regaining independence. Toward the end of the Goryeo PeriodKing Gongmin r. This exhibition will be based on the theme of Goryeo dogyeong, a report of the official trip that the envoy Xu Jing of the Chinese Song Dynasty gave in to the emperor after his visit to Goryeo as a member of the delegation.

You can see by which steps the portrait was donated to the museum and how it was processed for preservation.

Through the introductory remarks on the portrait written by Jo himself, light is shed on his life as a nobleman in the Joseon Period. At Gyeonggi Years Meet the Media, in Spring, you can enjoy the medley of beautiful collections of the museum and modern media art. Today, the ways of expressing cultural properties have become more diversified as more people are interested in media.

Read more.Have you imagined that artificial intelligence, which we thought nothing but machine learning, can do something very creative, like composing music? AI music generator sounds scary for a lot of people. Though many of us become more concerned about the future of human beings, only a few of us know how it actually works and how the related products perform.

Wanna enhance your videos through AI-powered programs? Read our Video Enhancer Review for more details. Using AI as a tool to make music or aid music composer has been in practice for quite some time. Back in the 90s, David Bowie helped develop an app called Verbasizer. This app took the literary source material and randomly reordered the words to create new combinations that could be used as lyrics.

Inresearchers at Sony used software called Flow Machines to create a melody in the style of The Beatles. The AI music generator applies deep learning networks, a type of AI that relies on analyzing large amounts of data. People have to feed software tons of source material covering various sorts of music.

The software then analyzes data to find patterns, like chords, tempo, length, and how notes relate to one another. By learning from all the input, then it can write its own melodies. On a micro-scale, the music produced by an AI music composer is convincing.

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When it comes to appreciation, they may disappoint you. You can use the following AI music generators to have a try to reach your own conclusion. During hours of the test, we list some AI music generators that we consider useful for you. Considering they are still something at the stage of development, though there are still some defects now, we think these AI music composers can develop and grow further.

Composition and music theory is also not needed here. From there, you can change the tempo, the key; mute individual instruments, or switch out entire instrument kits to shift the mood of the song its made. This could-based platform simplifies the process of creating soundtracks and helps users create music in a variety of genres by using AI-generated algorithms.

You can create an account on this platform to enjoy a free version. It seems that AIVA has a rich experience of composing emotional soundtracks for ads, video games or movies. You can use it to generate music without going through the music licensing process. Through this AI music composer, you can quickly make it automatically generate music by choosing a preset style and clicking Create. On top of that, people with a demand for a more-specialized music composer can find this helpful.

You can use it to edit existing songs. If you need more, however, the paid version can also give a big surprise. This online AI music generator has a very intuitive interface with a variety of scenes, moods, and genres.

After registration and subscription, you have to upload a video for which you want to generate music and select the style you like.If Mozart were alive today and if he was feeling a bit uninspired he might well sit down and produce a piece of music like this:. The tune is actually you guessed it the work of a machine-learning algorithm that was fed thousands of pieces of MIDI music as training data.

The algorithm, called MuseNetwas developed by researchers at OpenAI, a research company in San Francisco that's focused on researching intelligence and studying its potential impact. The researchers trained a very large neural network known as a transformer.

This type of network learns to predict the next few notes in a piece of music.


You can then give the network a few notes, and have it conjure up something new. It makes it possible to mix different genres and styles, and even to add and remove specific instruments. The work shows how effectively such a model can capture and reproduce statistical patterns that reflect the character of something like a piece of music.

The same researchers previous used similar technology to auto-generate text from a starting sentence. These results were sometimes remarkably realistic—prompting the researchers to fret a little dramatically about the risk that such a tool could be used to mass-produce fake news.


The MuseNet project is interesting from a music-history perspective, as it points to some interesting connections statistically speaking between different artists across genres and centuries. The tool is also quite fun to play with. Some people see great potential for this sort of technology to inspire new music. Sageev Oerea machine learning researcher at the University of Toronto who's interested in AI-generated music, was wowed by the tool's ability to riff on a famous piece of Mozart's.

OpenAI’s MuseNet AI generates novel 4-minute songs with 10 across a range of genres and styles

Very cool. It's true that tools like MuseNet may inspire new ways of making music. But how does it compare to human musical creativity? It has remarkable capacity to surprise, shock, and inspire. The algorithms have some ways to go yet. Updated April 27 with additional comment.

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Skip to Content. If Mozart were alive today and if he was feeling a bit uninspired he might well sit down and produce a piece of music like this: A piece of music, in the style of Mozart, generated by MuseNet. Latest content Load more.We've created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files.

MuseNet uses the same general-purpose unsupervised technology as GPT-2a large-scale transformer model trained to predict the next token in a sequence, whether audio or text. Since MuseNet knows many different styles, we can blend generations in novel ways. The model manages to blend the two styles convincingly, with the full band joining in at around the 30 second mark:. In simple mode shown by defaultyou'll hear random uncurated samples that we've pre-generated.

Choose a composer or style, an optional start of a famous piece, and start generating. This lets you explore the variety of musical styles the model can create. In advanced mode you can interact with the model directly. The completions will take longer, but you'll be creating an entirely new piece. We created composer and instrumentation tokens to give more control over the kinds of samples MuseNet generates.

During training time, these composer and instrumentation tokens were prepended to each sample, so the model would learn to use this information in making note predictions. At generation time, we can then condition the model to create samples in a chosen style by starting with a prompt such as a Rachmaninoff piano start:. We can visualize the embeddings from MuseNet to gain insight into what the model has learned.

Here we use t-SNE to create a 2-D map of the cosine similarity of various musical composer and style embeddings.

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MuseNet uses the recompute and optimized kernels of Sparse Transformer to train a layer network with 24 attention heads—with full attention over a context of tokens. This long context may be one reason why it is able to remember long-term structure in a piece, like in the following sample imitating Chopin:. Music generation is a useful domain for testing the Sparse Transformer as it sits on a middle ground between text and images.

Yet we can easily hear whether the model is capturing long term structure on the order of hundreds to thousands of tokens. We collected training data for MuseNet from many different sources. ClassicalArchives and BitMidi donated their large collections of MIDI files for this project, and we also found several collections online, including jazz, pop, African, Indian, and Arabic styles.

The transformer is trained on sequential data: given a set of notes, we ask it to predict the upcoming note. We experimented with several different ways to encode the MIDI files into tokens suitable for this task.

First, a chordwise approach that considered every combination of notes sounding at one time as an individual "chord", and assigned a token to each chord. Second, we tried condensing the musical patterns by only focusing on the starts of notes, and tried further compressing that using a byte pair encoding scheme.

We landed on an encoding that combines expressivity with conciseness: combining the pitch, volume, and instrument information into a single token. We also create an inner critic: the model is asked during training time to predict whether a given sample is truly from the dataset or if it is one of the model's own past generations.

This score is used to select samples at generation time.


We added several different kinds of embeddings to give the model more structural context.Username or Email Address. Remember Me. Have you heard of OpenAI? It made headlines in February when it showed off an AI model capable of writing news stories and fiction so convincingly-human, the company declined to release the research publicly in case the system was misused. A simple mode lets you choose a composer or style, and an optional start of a famous piece of music.

An advanced mode lets you interact with the model directly. Music Ally Ltd. Insight Countries Reports Sandbox. News News Jobs. Read More: News. July 13, at am. Leave a Reply All fields required. Related posts Read More.

Israeli startup MyPart aims to use AI to turn the long tail of music catalogues into revenue streams that humans may have overlooked. In fact, the company wants to do more than analyse music and pull the needle from the March 11, Read More. Loudly shows off AI tech to remix songs into different genres. March 5, Musiio launches self-service dashboard for AI catalogue-tagging. AI-tagging startup Musiio has launched a new service that will make it easier for music companies to use its technology. March 3, Contact Music Ally Ltd.

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Got it.Trying to generate music like Mozart, Beethoven, or perhaps Lady Gaga? AI research organization OpenAI just released a demo of a new deep learning algorithm that can automatically generate original music using many different instruments and styles. The algorithm was taught to discover patterns of harmony, rhythm, and style in the training dataset. ClassicalArchives and BitMidi donated their large collections of MIDI files for this project, and we also found several collections online, including jazz, pop, African, Indian, and Arabic styles.

The interactive demo, which uses NVIDIA Tesla V GPUs for inference, users can interact with the music generated by the algorithm, applying different instruments and sounds to generate an entirely new track. From there you can modify the instruments and modify the style of the track to make it sound like Mozart, or The Beatles, or Journey. OpenAI says the demo will be available through May 12, at that point they will make a decision on what direction the project goes based on feedback.

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