Music Transformer
- Music Transformer: Generating Music with Long-term Structure Online Supplement New sample Relatively Jazz. Piano-e-Competition Unconditioned Samples Relative Transformer.
- Transformers Energon: The music is sometimes pointed to as the series' only consistent saving grace. Transformers Cybertron had an awesome soundtrack, although the original Japanese version, Galaxy Force, and the US version (which was mostly unchanged, with a few exceptions such as the exclusion of the Japanese vocal songs) have individual fan.
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Google’s song-composing artificial intelligence (AI) might not measure up to Mozart or Liszt anytime soon, but it’s made impressive progress recently. In a blog post and accompanying paper (“Music Transformer“) this week, contributors to Project Magenta, a Google Brain project “exploring the role of machine learning as a tool in the creative process,” presented their work on Musical Transformer, a machine learning model that’s capable of generating relatively coherent tunes with a recognizable repetition.
“The Transformer, a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence,” the paper’s authors write. “This suggests that self-attention might also be well-suited to modeling music.”
Music Transformer NLL 0.335 # 2 Compare. Methods used in the Paper Edit Add Remove. METHOD TYPE; Sigmoid Activation Activation Functions Tanh Activation Activation Functions Residual Connection.
As the team explains, producing long pieces of music remains a challenge for AI because of its structural complexity; most songs contain multiple motifs, phrases, and repetition that neural networks have a tough time picking up on. And while previous work has managed to channel some of the self-reference observable in works composed by humans, it has relied on absolute timing signals, making it poorly suited for keeping track of themes that are based on relative distances and recurring intervals.
The team’s solution is Music Transformer, an “attention-based” neural network that creates “expressive” performances directly without first generating a score. By using an event-based representation and a technique known as relative attention, the Music Transformer is able not only to focus more on relational features, but generalize beyond the length of training samples with which it’s supplied. And because it’s less memory-intensive, it’s also able to generate longer musical sequences.
In tests, when primed with Chopin’s Black Key Etude, Music Transformer produced a song that was consistent in style throughout and contained multiple phrases sourced from the motif. By contrast, two previous algorithms — Performance RNN and Transformer — provided the same primer either lacked a discernable structure completely or failed to maintain a structure.
Here’s Music Transformer riffing on the above-mentioned Black Key Etude:
https://venturebeat.com/wp-content/uploads/2018/12/primed_chopin_moves_away.mp3https://venturebeat.com/wp-content/uploads/2018/12/primed_chopin_low_repetition.mp3And here’s it generating songs without a primer:
Music Transformer In To A Usb Drive
https://venturebeat.com/wp-content/uploads/2018/12/relatively_coherent.mp3https://venturebeat.com/wp-content/uploads/2018/12/relatively_passionate.mp3https://venturebeat.com/wp-content/uploads/2018/12/relatively_slow.mp3The team concedes that the Music Transformer is far from perfect — it sometimes produces songs with too much repetition, sparse sections, and odd jumps — but they’re hopeful it serves as a muse for musicians in need of inspiration.
Music Transformers
“This opens up the potential for users to specify their own primer and use the model as a creative tool to explore a range of possible continuations,” the team wrote.
Code for training and generating Music Transformer is forthcoming, they say, along with pre-trained checkpoints.
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Music Transformers Youtube
Music Transformer is an open source machine learning model from the Magenta research group at Google that can generate musical performances with some long-term structure. We find it interesting to see what these models can and can’t do, so we made this app to make it easier to explore and curate the model’s output.
If you listen to a bunch of samples, you’ll probably discover that most compositions (like a lot of fully AI-generated music) are nowhere near as good and interesting as what people compose. And that’s okay! We just thought it would be fun for more people to be able to hear them, think about them, or remix them in their own music. So here is a radio station of 100,000 random, not cherry-picked, samples. They are all downloadable as MIDI files under a CC0 (“No Rights Reserved”) license, so if you want to take one of the pieces and make it your own, do it -- we’d love to hear all about it!
For those interested in all the ones and zeros of this work, you can read more about the model in the blog post announcing it, the research paper it was published in, or the blog post about this app.
When you favourite a song, it is only stored locally, in your browser's cache, and not on any external server. In order to understand a bit better which pieces people like, skip over, or keep going back to, we are collecting some anonymous click analytics from this app in accordance with Google’s privacy policy. Download cognio driver.