Efficient bandwidth extension of musical signals using a differentiable harmonic plus noise model

被引：0

作者：

Pierre-Amaury Grumiaux

Mathieu Lagrange

机构：

[1] Nantes Université,

[2] École Centrale Nantes,undefined

[3] CNRS,undefined

[4] LS2N,undefined

[5] UMR 6004,undefined

来源：

EURASIP Journal on Audio, Speech, and Music Processing | / 2023卷

关键词：

Audio restoration; Deep learning; Differentiable sound model;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The task of bandwidth extension addresses the generation of missing high frequencies of audio signals based on knowledge of the low-frequency part of the sound. This task applies to various problems, such as audio coding or audio restoration. In this article, we focus on efficient bandwidth extension of monophonic and polyphonic musical signals using a differentiable digital signal processing (DDSP) model. Such a model is composed of a neural network part with relatively few parameters trained to infer the parameters of a differentiable digital signal processing model, which efficiently generates the output full-band audio signal.

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