Data-driven models for audio source separation such as U-Net or Wave-U-Net
are usually models dedicated to and specifically trained for a single task,
e.g. a particular instrument isolation. Training them for various tasks at once
commonly results in worse performances than training them for a single
specialized task. In this work, we introduce the Conditioned-U-Net (C-U-Net)
which adds a control mechanism to the standard U-Net. The control mechanism
allows us to train a unique and generic U-Net to perform the separation of
various instruments. The C-U-Net decides the instrument to isolate according to
a one-hot-encoding input vector. The input vector is embedded to obtain the
parameters that control Feature-wise Linear Modulation (FiLM) layers. FiLM
layers modify the U-Net feature maps in order to separate the desired
instrument via affine transformations. The C-U-Net performs different
instrument separations, all with a single model achieving the same performances
as the dedicated ones at a lower cost.

本篇论文提出了 Conditioned-U-Net 方法以及用于音频源分离的 FiLM 层，C-U-Net 控制机制允许我们训练一个唯一、通用的 U-Net 模型来执行多种乐器的分离，从而降低成本，实现同专门模型相同性能。

Conditioned-U-Net: 在 U-Net 中引入控制机制用于多源分离

Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for  Multiple Source Separations

Recent breakthroughs in computer vision and natural language processing have
spurred interest in challenging multi-modal tasks such as visual
question-answering and visual dialogue. For such tasks, one successful approach
is to condition image-based convolutional network computation on language via
Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and
shifting. We propose to generate the parameters of FiLM layers going up the
hierarchy of a convolutional network in a multi-hop fashion rather than all at
once, as in prior work. By alternating between attending to the language input
and generating FiLM layer parameters, this approach is better able to scale to
settings with longer input sequences such as dialogue. We demonstrate that
multi-hop FiLM generation achieves state-of-the-art for the short input
sequence task ReferIt --- on-par with single-hop FiLM generation --- while also
significantly outperforming prior state-of-the-art and single-hop FiLM
generation on the GuessWhat?! visual dialogue task.

本文提出了在卷积神经网络中多次迭代地生成 Feature-wise Linear Modulation（FiLM）层参数的方法，以更好地处理诸如对话等较长的输入序列，并在 GuessWhat？！视觉对话任务中显著优于之前的最新技术和单次 FiLM 生成。