A Neural Network Based Framework for Archetypical Sound SYnthesis

03/06/2020
by   Eric Guizzo, et al.
0

This paper describes a preliminary approach to algorithmically reproduce the archetypical structure adopted by humans to classify sounds. In particular, we propose an approach to predict the human perceived chaos/order level in a sound and synthesize new timbres that present the desired amount of this feature. We adopted a Neural Network based method, in order to exploit its inner predisposition to model perceptive and abstract features. We finally discuss the obtained accuracy and possible implications in creative contexts.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/25/2019

Neural Percussive Synthesis Parameterised by High-Level Timbral Features

We present a deep neural network-based methodology for synthesising perc...
research
07/08/2022

Braid-based architecture search

In this article, we propose the approach to structural optimization of n...
research
05/23/2022

A neural network based controller for underwater robotic vehicles

Due to the enormous technological improvements obtained in the last deca...
research
08/25/2020

JokeMeter at SemEval-2020 Task 7: Convolutional humor

This paper describes our system that was designed for Humor evaluation w...
research
09/05/2020

Reverse-engineering Bar Charts Using Neural Networks

Reverse-engineering bar charts extracts textual and numeric information ...
research
04/06/2017

The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study

A neural network-based chart pattern represents adaptive parametric feat...
research
09/16/2020

SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph

As a novel contribution to the problem of semantifying biological assays...

Please sign up or login with your details

Forgot password? Click here to reset