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arXiv · 2104.13553

AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries

Woosung Choi·Minseok Kim·Marco A. Martínez Ramírez·Jaehwa Chung·Soonyoung Jung
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Abstract

This paper proposes a neural network that performs audio transformations to user-specified sources (e.g., vocals) of a given audio track according to a given description while preserving other sources not mentioned in the description. Audio Manipulation on a Specific Source (AMSS) is challenging because a sound object (i.e., a waveform sample or frequency bin) is `transparent'; it usually carries information from multiple sources, in contrast to a pixel in an image. To address this challenging problem, we propose AMSS-Net, which extracts latent sources and selectively manipulates them while preserving irrelevant sources. We also propose an evaluation benchmark for several AMSS tasks, and we show that AMSS-Net outperforms baselines on several AMSS tasks via objective metrics and empirical verification.

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BibTeXRIS

Woosung Choi, Minseok Kim, Marco A. Martínez Ramírez, Jaehwa Chung, Soonyoung Jung. 2021-04-28. AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries. https://arxiv.org/abs/2104.13553

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