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TF Representations and Masking — Open-Source Tools & Data for Music Source Separation

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Masking has many uses in different aspects computer science and machine learning like language modelling and computer vision. It is also an essential part of how many modern source separation approaches approximate sources from a mixture. To separate a single source, a separation approach must create a single mask. To separate multiple sources, a separation approach must create multiple masks. Masks are most commonly used with approaches that process a TF Representation, however you could make the case that some waveform-based deep learning architectures use masking within specific parts of their network (for instance see these papers: [LM18,LM19]). The content in this section, however, applies specifically to masking TF Representations. For reasons we will fully discuss in the next section, we only apply masks to the magnitude values of a TF Representation, i.e., we do no apply masks to the phase component of an STFT. Because of this, in this section we will focus on masks applied to

TF Representations and Masking — Open-Source Tools & Data for Music Source Separation Open-Source Tools & Data for Music Source Separation Powered by Jupyter Book .md .pdf repository open issue to have style consistency --> Contents TF Representations and Masking ¶ Masking has many uses in different aspects computer science and machine learning like language modelling and computer vision. It is also an essential part of how many modern source separation approaches approximate sources from a mixture. To separate a single source, a separation approach must create a single mask. To separate

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