Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

Abstract

Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.

References in corpus (1)

• Improving neural networks by preventing co-adaptation of feature detectors

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• Learning Multi-instrument Classification with Partial Labels
• Receptive-Field Regularized CNNs for Music Classification and Tagging
• Timbre Classification of Musical Instruments with a Deep Learning Multi-Head Attention-Based Model
• Multitask learning for frame-level instrument recognition