Главная
Study mode:
on
1
Introduction
2
Motivation
3
Demo
4
Additive Synthesis
5
Subtractive Synthesis
6
Wavetable Synthesis
7
FM Synthesis
8
Other Methods
9
Why Parameter Inference
10
Parameters
11
References
12
Deep Learning Basics
13
Neural Network Blocks
14
Why Deep Learning
15
Building a Dataset
16
Syntheon
17
Json
18
Cnns
19
Serums
20
Other works
21
Selfsupervised learning
22
Differentiability
23
Differentiable DSP
24
Semisupervised learning
25
Discussion
26
Summary
27
References Shoutouts
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a conference talk on parameter inference of music synthesizers using deep learning techniques. Delve into the potential of automating synthesizer preset generation based on desired audio samples. Examine recent research applying deep learning to various synthesizer types, including FM and wavetable. Discover the challenges faced in this field and gain insights into neural network basics, dataset building, and advanced learning approaches like self-supervised and semi-supervised learning. Learn about differentiable DSP and its applications in sound design. Ideal for audio developers, music producers, and machine learning enthusiasts interested in the intersection of AI and music technology.

Parameter Inference of Music Synthesizers with Deep Learning

ADC - Audio Developer Conference
Add to list
#Computer Science #Deep Learning #Artificial Intelligence #Neural Networks #Engineering #Electrical Engineering #Digital Signal Processing #Audio Processing #Art & Design #Music #Music Production #Synthesizers #Audio Production #Sound Synthesis #Wavetable Synthesis
1 of 27

Introduction