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 an instrument
recognition module that conditions the other two modules: a transcription
module that outputs instrument-specific piano rolls, and a source separation
module that utilizes instrument information and transcription results. The
joint training of the transcription and source separation modules serves to
improve the performance of both tasks. The instrument module is optional and
can be directly controlled by human users. This makes Jointist a flexible
user-controllable framework. Our challenging problem formulation makes the
model highly useful in the real world given that modern popular music typically
consists of multiple instruments. Its novelty, however, necessitates a new
perspective on how to evaluate such a model. In our experiments, we assess the
proposed model from various aspects, providing a new evaluation perspective for
multi-instrument transcription. Our subjective listening study shows that
Jointist achieves state-of-the-art performance on popular music, outperforming
existing multi-instrument transcription models such as MT3. We conducted
experiments on several downstream tasks and found that the proposed method
improved transcription by more than 1 percentage points (ppt.), source
separation by 5 SDR, downbeat detection by 1.8 ppt., chord recognition by 1.4
ppt., and key estimation by 1.4 ppt., when utilizing transcription results
obtained from Jointist.
Demo available at https://jointist.github.io/Demo.

本文提出 Jointist，一种仪器感知的多仪器框架，可以从音频剪辑中转录、识别和分离多个乐器。Jointist 的新颖性质对于评估这种模型提出了新的视角，然而，在我们的实验中，该模型在多个下游任务上实现了性能的提升，包括转录，分离，下行拍检测，和弦识别和关键识别。

Jointist: 通过联合训练同时提高多乐器转录和音乐源分离

Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training

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。