Frame-level Instrument Recognition by Timbre and Pitch

Abstract

Instrument recognition is a fundamental task in music information retrieval, yet little has been done to predict the presence of instruments in multi-instrument music for each time frame. This task is important for not only automatic transcription but also many retrieval problems. In this paper, we use the newly released MusicNet dataset to study this front, by building and evaluating a convolutional neural network for making frame-level instrument prediction. We consider it as a multi-label classification problem for each frame and use frame-level annotations as the supervisory signal in training the network. Moreover, we experiment with different ways to incorporate pitch information to our model, with the premise that doing so informs the model the notes that are active per frame, and also encourages the model to learn relative rates of energy buildup in the harmonic partials of different instruments. Experiments show salient performance improvement over baseline methods. We also report an analysis probing how pitch information helps the instrument prediction task. Code and experiment details can be found at \url{https://biboamy.github.io/instrument-recognition/}.

References in corpus (1)

• Learning Features of Music from Scratch

Cited by in corpus (10)

• Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music
• An Attention Mechanism for Musical Instrument Recognition
• Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition
• Modeling Music Modality with a Key-Class Invariant Pitch Chroma CNN
• Multitask learning for instrument activation aware music source separation
• Learning Multi-instrument Classification with Partial Labels
• Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes
• Visual Attention for Musical Instrument Recognition
• Timbre Classification of Musical Instruments with a Deep Learning Multi-Head Attention-Based Model
• Multitask learning for frame-level instrument recognition