Standard architectures require training separate models for different types of audio sources. 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.ĭeep learning techniques for separating audio into different sound sources face several challenges. We also argue that transcription models can be utilized as a preprocessing module for other music analysis tasks. During the experiment, we assess the model from various aspects, providing a new evaluation perspective for multi-instrument transcription. However, its novelty necessitates a new perspective on how to evaluate such a model. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. 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. 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.

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.

Seventh string transcribe license key