Recent deep-learning approaches have shown that Frequency Transformation (FT)
blocks can significantly improve spectrogram-based single-source separation
models by capturing frequency patterns. The goal of this paper is to extend the
FT block to fit the multi-source task. We propose the Latent Source Attentive
Frequency Transformation (LaSAFT) block to capture source-dependent frequency
patterns. We also propose the Gated Point-wise Convolutional Modulation
(GPoCM), an extension of Feature-wise Linear Modulation (FiLM), to modulate
internal features. By employing these two novel methods, we extend the
Conditioned-U-Net (CUNet) for multi-source separation, and the experimental
results indicate that our LaSAFT and GPoCM can improve the CUNet's performance,
achieving state-of-the-art SDR performance on several MUSDB18 source separation
tasks.

本文提出了两个创新的方法，即 LaSAFT 和 GPoCM，扩展了 Conditioned-U-Net（CUNet）进行多源分离，实验结果表明，我们的方法可以显著提高在多个 MUSDB18 来源分离任务中的性能，达到了最先进的 SDR 性能。

LaSAFT: 基于潜在空间的源自适应频率转换技术用于条件源分离

LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned  Source Separation

Time-frequency (T-F) domain masking is a mainstream approach for
single-channel speech enhancement. Recently, focuses have been put to phase
prediction in addition to amplitude prediction. In this paper, we propose a
phase-and-harmonics-aware deep neural network (DNN), named PHASEN, for this
task. Unlike previous methods that directly use a complex ideal ratio mask to
supervise the DNN learning, we design a two-stream network, where amplitude
stream and phase stream are dedicated to amplitude and phase prediction. We
discover that the two streams should communicate with each other, and this is
crucial to phase prediction. In addition, we propose frequency transformation
blocks to catch long-range correlations along the frequency axis. The
visualization shows that the learned transformation matrix spontaneously
captures the harmonic correlation, which has been proven to be helpful for T-F
spectrogram reconstruction. With these two innovations, PHASEN acquires the
ability to handle detailed phase patterns and to utilize harmonic patterns,
getting 1.76dB SDR improvement on AVSpeech + AudioSet dataset. It also achieves
significant gains over Google's network on this dataset. On Voice Bank + DEMAND
dataset, PHASEN outperforms previous methods by a large margin on four metrics.

本文介绍了一种基于深度神经网络的相位和谐感知模型（PHASEN），用于单通道语音增强，其中使用两种不同的流进行幅度和相位预测，并设计了频率转换模块来捕捉沿频率轴的长程相关性，在 AVSpeech + AudioSet 和 Voice Bank + DEMAND 数据集上分别获得 1.76dB 和大幅优于以前方法的表现。