Musical Instrument Recognition with a Convolutional Neural Network and Staged Training

Abstract

Musical instrument recognition is an important task within the broad field of Music Information Retrieval. It helps to build recommendation systems, compute similarity between musical compositions and enable automatic search in music collections with regard to the instrument. The task has two variants differed by the difficulty level. The simpler one is classification based on the sound of a single instrument, while the more difficult challenge is to recognize the predominant instrument in polyphonic recordings. In this paper, we used a convolutional neural network to solve both of these problems. As the analysis of monotimbral recordings is relatively easy, we used the knowledge acquired during solving it to train a neural network to tackle the more complex predominant instrument recognition problem. Within this staged training scenario, we also examined the impact of introducing some intermediate stages during the training sessions. The results showed that such a training approach has a potential to improve the accuracy of classification.