In this paper, we introduce the MoisesDB dataset for musical source
separation. It consists of 240 tracks from 45 artists, covering twelve musical
genres. For each song, we provide its individual audio sources, organized in a
two-level hierarchical taxonomy of stems. This will facilitate building and
evaluating fine-grained source separation systems that go beyond the limitation
of using four stems (drums, bass, other, and vocals) due to lack of data. To
facilitate the adoption of this dataset, we publish an easy-to-use Python
library to download, process and use MoisesDB. Alongside a thorough
documentation and analysis of the dataset contents, this work provides baseline
results for open-source separation models for varying separation granularities
(four, five, and six stems), and discuss their results.

本文介绍了用于音乐源分离的 MoisesDB 数据集，包括 240 首歌曲，来自 45 位艺术家，覆盖了十二种音乐流派。我们提供了每首歌曲的个体音频源，以两级分层分类法进行组织。为了方便构建和评估细粒度的源分离系统，我们发布了一个易于使用的 Python 库来下载、处理和使用 MoisesDB。除了详细介绍和分析数据集内容外，本研究还提供了针对不同分离粒度（四、五和六个音频源）的开源分离模型的基线结果，并讨论了它们的结果。

Moisesdb: 超越 4 音轨的源分离数据集

Moisesdb: A dataset for source separation beyond 4-stems

Supervised deep learning approaches to underdetermined audio source
separation achieve state-of-the-art performance but require a dataset of
mixtures along with their corresponding isolated source signals. Such datasets
can be extremely costly to obtain for musical mixtures. This raises a need for
unsupervised methods. We propose a novel unsupervised model-based deep learning
approach to musical source separation. Each source is modelled with a
differentiable parametric source-filter model. A neural network is trained to
reconstruct the observed mixture as a sum of the sources by estimating the
source models' parameters given their fundamental frequencies. At test time,
soft masks are obtained from the synthesized source signals. The experimental
evaluation on a vocal ensemble separation task shows that the proposed method
outperforms learning-free methods based on nonnegative matrix factorization and
a supervised deep learning baseline. Integrating domain knowledge in the form
of source models into a data-driven method leads to high data efficiency: the
proposed approach achieves good separation quality even when trained on less
than three minutes of audio. This work makes powerful deep learning based
separation usable in scenarios where training data with ground truth is
expensive or nonexistent.

本文提出了一种无监督的基于模型的深度学习方法，用于音频源分离，该方法通过参数化源过滤器模型对每个源进行建模，并使用神经网络以基本频率估计源模型的参数来重构观察到的混合音频信号，实验证明该方法具有较高的数据效率和好的分离效果。

可微分参数源模型的无监督音乐源分离

Unsupervised Music Source Separation Using Differentiable Parametric Source Models

Isolating individual instruments in a musical mixture has a myriad of
potential applications, and seems imminently achievable given the levels of
performance reached by recent deep learning methods. While most musical source
separation techniques learn an independent model for each instrument, we
propose using a common embedding space for the time-frequency bins of all
instruments in a mixture inspired by deep clustering and deep attractor
networks. Additionally, an auxiliary network is used to generate parameters of
a Gaussian mixture model (GMM) where the posterior distribution over GMM
components in the embedding space can be used to create a mask that separates
individual sources from a mixture. In addition to outperforming a
mask-inference baseline on the MUSDB-18 dataset, our embedding space is easily
interpretable and can be used for query-based separation.

该论文介绍一种使用深度聚类和深度吸引子网络的方法，将音乐混合中的所有乐器的时频图嵌入到一个共同的嵌入空间中，并使用混合高斯模型生成参数，从而实现对音乐源的分离，同时嵌入空间具有易于解释性。