A Context Encoder For Audio Inpainting
| Autor: | Nicki Holighaus, Nathanaël Perraudin, Andrés Marafioti, Piotr Majdak |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Audio signal
Acoustics and Ultrasonics Computer science Speech recognition Inpainting Context (language use) Linear predictive coding Signal 030507 speech-language pathology & audiology 03 medical and health sciences Computational Mathematics Frequency domain Computer Science (miscellaneous) Electrical and Electronic Engineering 0305 other medical science Focus (optics) Encoder |
| Zdroj: | IEEE/ACM Transactions on Audio, Speech, and Language Processing. 27:2362-2372 |
| ISSN: | 2329-9304 2329-9290 |
| Popis: | In this article, we study the ability of deep neural networks (DNNs) to restore missing audio content based on its context, i.e., inpaint audio gaps. We focus on a condition which has not received much attention yet: gaps in the range of tens of milliseconds. We propose a DNN structure that is provided with the signal surrounding the gap in the form of time-frequency (TF) coefficients. Two DNNs with either complex-valued TF coefficient output or magnitude TF coefficient output were studied by separately training them on inpainting two types of audio signals (music and musical instruments) having 64-ms long gaps. The magnitude DNN outperformed the complex-valued DNN in terms of signal-to-noise ratios and objective difference grades. Although, for instruments, a reference inpainting obtained through linear predictive coding performed better in both metrics, it performed worse than the magnitude DNN for music. This demonstrates the potential of the magnitude DNN, in particular for inpainting signals that are more complex than single instrument sounds. |
| Databáze: | OpenAIRE |
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