A typical neural speech enhancement (SE) approach mainly handles speech and
noise mixtures, which is not optimal for singing voice enhancement scenarios.
Music source separation (MSS) models treat vocals and various accompaniment
components equally, which may reduce performance compared to the model that
only considers vocal enhancement. In this paper, we propose a novel multi-band
temporal-frequency neural network (MBTFNet) for singing voice enhancement,
which particularly removes background music, noise and even backing vocals from
singing recordings. MBTFNet combines inter and intra-band modeling for better
processing of full-band signals. Dual-path modeling are introduced to expand
the receptive field of the model. We propose an implicit personalized
enhancement (IPE) stage based on signal-to-noise ratio (SNR) estimation, which
further improves the performance of MBTFNet. Experiments show that our proposed
model significantly outperforms several state-of-the-art SE and MSS models.

我们提出了一种新颖的多频带时间频率神经网络（MBTFNet），用于歌声增强，可以从歌曲录音中去除背景音乐、噪音甚至伴唱，实验证明我们的模型在增强效果上显著优于几种最先进的声音增强和音乐源分离模型。

MBTFNet：多频段时间频率神经网络用于歌声增强

MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice  Enhancement

Modern neural speech enhancement models usually include various forms of
phase information in their training loss terms, either explicitly or
implicitly. However, these loss terms are typically designed to reduce the
distortion of phase spectrum values at specific frequencies, which ensures they
do not significantly affect the quality of the enhanced speech. In this paper,
we propose an effective phase reconstruction strategy for neural speech
enhancement that can operate in noisy environments. Specifically, we introduce
a phase continuity loss that considers relative phase variations across the
time and frequency axes. By including this phase continuity loss in a
state-of-the-art neural speech enhancement system trained with reconstruction
loss and a number of magnitude spectral losses, we show that our proposed
method further improves the quality of enhanced speech signals over the
baseline, especially when training is done jointly with a magnitude spectrum
loss.

本文提出了一种有效的相位重建策略，可以在嘈杂的环境中操作，通过将相位连续性损失考虑在最先进的神经语音增强系统中，本文证明了我们所提出的方法在训练过程中，可以显著提高增强语音信号的质量。