In this paper, we introduce Jointist, an instrument-aware multi-instrument
framework that is capable of transcribing, recognizing, and separating multiple
musical instruments from an audio clip. Jointist consists of the instrument
recognition module that conditions the other modules: the transcription module
that outputs instrument-specific piano rolls, and the source separation module
that utilizes instrument information and transcription results.
The instrument conditioning is designed for an explicit multi-instrument
functionality while the connection between the transcription and source
separation modules is for better transcription performance. Our challenging
problem formulation makes the model highly useful in the real world given that
modern popular music typically consists of multiple instruments. However, its
novelty necessitates a new perspective on how to evaluate such a model. During
the experiment, we assess the model from various aspects, providing a new
evaluation perspective for multi-instrument transcription. We also argue that
transcription models can be utilized as a preprocessing module for other music
analysis tasks. In the experiment on several downstream tasks, the symbolic
representation provided by our transcription model turned out to be helpful to
spectrograms in solving downbeat detection, chord recognition, and key
estimation.

该研究提出了一种多乐器转录、识别和分离的方法，并对其进行了评估。该方法可以作为其他音乐分析任务的预处理模块，并在下游任务中提供了有益的符号表示。

Jointist: 多乐器转录的联合学习及其应用

Jointist: Joint Learning for Multi-instrument Transcription and Its Applications

We present a semantic vector space model for capturing complex polyphonic
musical context. A word2vec model based on a skip-gram representation with
negative sampling was used to model slices of music from a dataset of
Beethoven's piano sonatas. A visualization of the reduced vector space using
t-distributed stochastic neighbor embedding shows that the resulting embedded
vector space captures tonal relationships, even without any explicit
information about the musical contents of the slices. Secondly, an excerpt of
the Moonlight Sonata from Beethoven was altered by replacing slices based on
context similarity. The resulting music shows that the selected slice based on
similar word2vec context also has a relatively short tonal distance from the
original slice.

本文基于 word2vec 模型提出了一种用于捕捉复杂音乐语境的向量空间模型，针对 Beethoven 的钢琴奏鸣曲数据集，利用 t 分布随机邻域嵌入可视化分析证明该模型成功捕捉到了音调关系。在一个类似的 word2vec 语境下，替换后的 Moonlight Sonata 片段与原始片段的音调差距相对较短。