IEEE Circuits and Systems Magazine - Q4 2019 - 19
Feature
Applications of
Deep Learning to
Audio Generation
Abstract
In the recent past years, deep learning based machine learning
systems have demonstrated remarkable success for a wide range
of learning tasks in multiple domains such as computer vision,
speech recognition and other pattern recognition based applications. The purpose of this article is to contribute a timely review
and introduction of state-of-the-art deep learning techniques and
their effectiveness in speech/acoustic signal processing. Thorough
investigations of various deep learning architectures are provided
under the categories of discriminative and generative algorithms,
including the up-to-date Generative Adversarial Networks (GANs)
as an integrated model. A comprehensive overview of applications
in audio generation is highlighted. Based on understandings from
Digital Object Identifier 10.1109/MCAS.2019.2945210
Date of current version: 19 November 2019
FOURTH QUARTER 2019
1531-636X/19©2019IEEE
these approaches, we discuss how deep learning methods can
benefit the field of speech/acoustic signal synthesis and the potential issues that need to be addressed for prospective real-world
scenarios. We hope this survey provides a valuable reference for
practitioners seeking to innovate in the usage of deep learning approaches for speech/acoustic signal generation.
I. Introduction
ince the phonograph was invented in 1877 by Edison, different types of acoustic sounds like voice,
music and birdsong can be preserved as audio
recordings [1]. After decades of exploration and analysis,
essential properties of various acoustic signals have been
revealed for a deep understanding of the mechanism for
production, propagation and perception. The diverse
S
IEEE CIRCUITS AND SYSTEMS MAGAZINE
©ISTOCKPHOTO.COM/JIRAROJ PRADITCHAROENKUL
Yuanjun Zhao, Student Member, IEEE, Xianjun Xia, Student Member, IEEE,
and Roberto Togneri Senior Member, IEEE
19
IEEE Circuits and Systems Magazine - Q4 2019
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