© 2020 Springer Nature Switzerland AG. At the end of the night I’d send him my latest version, and by the time I got up the next morning he had sent me some new ideas back.Below is the (almost) final arrangement in Maschine. For the accompanying image I thought it would be a nice touch to run the original “That’s one really cool thing about collaborating. The yellow box is a single RNN unit where ‘C_i’ is a first character input given to RNN unit. Note, the above image shows time-unwrapping of RNN. ).But be warned if you’re curious about the code. About C4Media To do this, the researchers used the Inverse Short Time Fourier Transform method. This was not really meant to be shared. This book is a survey and analysis of how deep learning can be used to generate musical content. This article talks about music generation using deep learning in Python. For example it only considers the beginning of a sample. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. Cocktail Party Source Separation Using Deep Learning Networks Isolate a speech signal using a deep learning network. Then in next time-step we will give ‘c’ as an input and expects ‘a’ as an output and so on. Azure + Spring Boot = Serverless - Q&A with Julien Dubois “emb2_13727 — StylusBD08 023_9519 — Kick JunkieXL 3.wav” in “generated_emb_big2” was conditioned on the two files “StylusBD08 023” and “Kick JunkieXL 3” (and the other numbers refer to my own internal indexing system). The fact that we were in completely different time zones (D.A.V.E. “generated_1” and “generated_2” are unconditioned models, whereas “generated_emb2” and “generated_emb_big2” are conditioned models (the number at the end refers to the batch size I trained them with).The result is quite a nice harmonic sound, but the kick adds some interesting subby attack at the beginning, giving a nice stabby synth sound.Not quite sure yet what direction I wanted to take this in, I again got some inspiration from As a simple example: I might have a particular snare sound, but I would like to replace it with a similar but slightly different sound.I was actually quite surprised how nice the clusters turned out, and also which clusters are adjacent to which (like the clap, hat, snare transition).The Beauty of Bayesian Optimization, Explained in Simple TermsAll the code for the VAE, as well as some of the preprocessing and other random bits Also quite a few of the samples, especially the VAE generated ones, had some unpleasant high frequency noise so I applied some low pass filters to get rid of that.Here a single model allowed me to create embeddings that I can use to condition another model, enabled me to do advanced semantic search, as well as actually generate novel sounds by itself.If I keep working on this, one thing I might want to try is not to encode the entire spectrogram and use it as global conditioning, but encode individual time slices and then feed them as local conditioning to the Wavenet to get some more control over the temporal aspects of the sound.To give the Wavenet a better chance to understand what data it was supposed to generate, I decided to add a conditioning network.Having all the models in place, I could start to actually generate some sounds.The second way was to actually combine two or more embeddings, and then decode the resulting latent code. In the last layer we will keep “Softmax” activations. We will keep on concatenating the output character and generate music of some length. In the Frame Relation Network they tried to capture the detail transformations and actions of the objects with less computational time. Then I thought maybe start with some melodic stuff. For some movie events they generated sounds themselves inside a studio (such as cutting, footsteps, and a clock sound).