Answers.Body: <p>The first thing is to define what is a «good» and a «bad» sound. This is an extremely tricky issue, since the networks need <em>numeric</em> inputs. And music is whole bunch of numbers.</p>
  
  <p>I know from people doing research in identifying <em>how similar</em> two sounds are, and imitation, say: you hear a sound and try to make another that sounds like it. Like when you hum a song or similar. That is by no means easy. These guys are using something similar to feature extraction, with Fourier transforms and energy and <a href="https://en.wikipedia.org/wiki/Music_information_retrieval" rel="noreferrer">such things</a>. They feed the networks with the (selected) features and... Train. </p>
  
  <p>Now, to return to your original question: *What do you present as <em>target</em> during training?* You can present different <em>types</em> of music as categories and classify (I couldn't help but think on <a href="https://link.springer.com/article/10.3758/BF03192900" rel="noreferrer">this research with fish</a>). Or <strong>you</strong> define categories of music <strong>you</strong> like and see if the network can classify them ;)</p>
  
  <p>One basic decision here is how long you get a piece of sound. Since it is needed to analyse frequency, this is a key issue. Since you talked about DNN, I was wondering if you wanted to do it <em>online</em>, as a stream, in which case I don't have the slightest idea where to begin, other than do it <em>after a little while</em>.</p>
  
  <hr>
  
  <p>Other idea: I remember a little sketch in <a href="http://www.bbc.co.uk/programmes/b012xppj" rel="noreferrer">this series</a> about a researcher that makes use of the relations between peaks in the Fourier spectrum in order to differentiate noise from music.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-05T12:05:13.230Z
Answers.LastActivityDate: 2016-08-05T12:05:13.230Z
Answers.UserDisplayName: Luis
Body: <p>DNNs are typically used to classify things (of course) but can we let them go wild with sounds and then tell them if we think it sounds good or not? I'd like to think after a training class has been made (perhaps comparing the output to an existing song) we could get an NN that has a basic concept of music.</p>
  
  <p>Timing would be an issue; I'm not sure how feasible this is. A strongly weighted input attached to all hidden layers perhaps? Use it as the bias?</p>
  
  <p>Is this even slightly feasible? </p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-05T09:19:33.500Z
Comments.Text: Google recently released a package in the area of music and art its called `Magenta`. I think its possible to classify a sound is good or not if we have proper dataset.
Comments.UserDisplayName: Eka
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-05T11:49:47.297Z
Comments.Text: DNN := Dynamic Neural Networks, right? Aka. Recurrent Neural Networks?
Comments.UserDisplayName: Luis
Tags: <deep-neural-networks><machine-learning>
Title: Has any research been done on DNN Music?
UserDisplayName: Andrew
ThreadText: # Has any research been done on DNN Music?
  
  (Asked by: username_0 on 2016-08-05)
  
  DNNs are typically used to classify things (of course) but can we let them go wild with sounds and then tell them if we think it sounds good or not? I'd like to think after a training class has been made (perhaps comparing the output to an existing song) we could get an NN that has a basic concept of music.
  
  Timing would be an issue; I'm not sure how feasible this is. A strongly weighted input attached to all hidden layers perhaps? Use it as the bias?
  
  Is this even slightly feasible?
  
  **Tags:** deep-neural-networks, machine-learning
  > **Comments:**
  > **username_1**: Google recently released a package in the area of music and art its called `Magenta`. I think its possible to classify a sound is good or not if we have proper dataset.  
  > **username_2**: DNN := Dynamic Neural Networks, right? Aka. Recurrent Neural Networks?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2016-08-05. Score: 6 | Accepted answer
  
  The first thing is to define what is a «good» and a «bad» sound. This is an extremely tricky issue, since the networks need *numeric* inputs. And music is whole bunch of numbers.
  
  I know from people doing research in identifying *how similar* two sounds are, and imitation, say: you hear a sound and try to make another that sounds like it. Like when you hum a song or similar. That is by no means easy. These guys are using something similar to feature extraction, with Fourier transforms and energy and [such things](https://en.wikipedia.org/wiki/Music_information_retrieval). They feed the networks with the (selected) features and... Train.
  
  Now, to return to your original question: \*What do you present as *target* during training?\* You can present different *types* of music as categories and classify (I couldn't help but think on [this research with fish](https://link.springer.com/article/10.3758/BF03192900)). Or **you** define categories of music **you** like and see if the network can classify them ;)
  
  One basic decision here is how long you get a piece of sound. Since it is needed to analyse frequency, this is a key issue. Since you talked about DNN, I was wondering if you wanted to do it *online*, as a stream, in which case I don't have the slightest idea where to begin, other than do it *after a little while*.
  
  ---
  
  Other idea: I remember a little sketch in [this series](http://www.bbc.co.uk/programmes/b012xppj) about a researcher that makes use of the relations between peaks in the Fourier spectrum in order to differentiate noise from music.
  
  
  ---
  
  