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This paper proposes a neural network that performs audio transformations touser-specified sources (e.g., vocals) of a given audio track according to agiven description while preserving other sources not mentioned in thedescription. Audio Manipulation on a Specific Source (AMSS) is challengingbecause a sound object (i.e., a waveform sample or frequency bin) is`transparent'; it usually carries information from multiple sources, incontrast to a pixel in an image. To address this challenging problem, wepropose AMSS-Net, which extracts latent sources and selectively manipulatesthem while preserving irrelevant sources. We also propose an evaluationbenchmark for several AMSS tasks, and we show that AMSS-Net outperformsbaselines on several AMSS tasks via objective metrics and empiricalverification.

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