Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks
This addresses the problem of reducing reliance on domain expertise and manual feature engineering in music information retrieval for researchers and practitioners.
The paper tackled automatic musical instrument identification in polyphonic music by applying convolutional neural networks (CNNs) to raw audio, achieving performance that surpasses traditional methods relying on hand-crafted features.
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.