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Comment by krosaen | Hacker News Reader

I like the concept and the framing of the demo is really cool, but the actual music generated in response to me doing basic things like playing a blues riff or a pentatonic scale was usually just a repeat of some fragment of what I was playing. It felt like something that could be built using a basic heuristic...

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Yeah either the examples from the video are cherry-picked, or something's wrong with the deployment, because what I get is way worse than what's in the video.

I really don't like this new trend of throwing Deep Learning at anything without really checking whether a simple model (e.g. markov chains in this case) would give equal results. Because using a simple model wouldn't be "cool" or newsworthy...

When you've described the entire new vision of your company as being "doing things with deep learning", you aren't going to bother seeing if the simpler solution works. Just isn't good for business.

But then a competitor comes along, doing the same thing with vastly less complex model and capable of offering cheaper/faster service because of that.

Here's a relevant excerpt from research I did on college into this. FWIW, my research was specifically into genetic algorithms and generating similar musical styles as the input music. It didn't perform particularly well but had some surprises.

From my research, a Markov model could potentially be viable for this demo in particular. However, it wouldn't be viable for longer generation of music where you're trying to produce an actual song. The biggest problem being that it doesn't have the concept of melody in it.

--- research after here ---

A Markov model utilized by Herfort and Rehberger [17] uses the immediate previous note in order to determine the next note against a set of probabilities. This is a fairly simple algorithm as the set of rules is not large, but it is extremely short-sighted by nature. This “first order” Markov chain is not capable of determining a probability based on a large sequence of pitches. To counteract this, “N-order” Markov chain models can be determined to look more deeply into the past, but the complexity of the rule set increases exponentially with added depth. Further, every aspect of music that is to be modeled would require its own Markov model. For instance, there would need to be a model for pitch, duration, etc. This leads to a potentially huge Markov model rule set where all chains are independent of each other. Thus, Markov chains are good at composing musical melodies on the micro level (such as a measure) but fail to produce quality music at the macro level.

[17] Rehberger, Leopold. “Composing music using AI algorithms.” GRIN Publishing GmbH. 2004. <http://www.grin.com/en/e-book/108991/composing-music-using-a....

Source: https://github.com/sb8244/GeneticMusicComposition

Just to complete your post, one problem I always have with music generation using "first-order" Markov Chains is that they get really confused by children's music. For example, here's the start of "twinkle twinkle, little star":

CCGG AAG- FFEE DDC-

So the tranistion matrix contails rules like: P(C -> C) = 50% P(A -> A) = 50% P(F -> F) = 50% P(E -> E) = 50%

Which makes huge loops of the same note quite common. This makes for some really uncanny music.

Agreed, it seems to either play a note you've played or pick a random interval. The interval and timing perhaps aren't truly random but it makes no difference to the listener's ear. Doesn't appear to have any concept of, or the ability to learn, scales, rhythm, or melody (or chords). Neat idea, but akin to playing piano with a 5-year-old (perhaps not as fun).

Of course that may change as people could be expanding its training through their interactions with it.

Yeah, had the same impression

It seems it almost gets the concept of tone, but barely

And for some reason it worked on Firefox, but not Chrome (it doesn't even load for me)

even going a simple jingle bells loop (I know, I haven't studied much) get's a pretty much nonsensical repeat response.

on a side note I'd like to have an option to put ai on mute and have the duet run after a session, since it's extremely disorienting for a noob to play with random noise on the feedback loop

It almost felt random at times.

Could just be a limitation of the training resources available.

I don't think there's a practical limitation on training resources with respect to Music. Maybe the authors didn't get into any of the rubric, but it's certainly out there. Gobs of it.

By resources, I meant training time, CPU/GPU resources, not availability of data.

At Google? Ha.

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