Reconhecimento de instrumentos musicais utilizando machine learning: um estudo de caso.

Abstract

The field of Music Information Retrieval (MIR) encompasses a variety of topics, including music transcription, source separation, and recognition of instruments and/or musical genres. A practical example of this field is Spotify, which uses recommendation systems capable of learning patterns of the reproduced content and suggesting similar music to users. However, instrument recognition can still present challenges depending on the dataset used, making pattern recognition more difficult. In this context, the objective of this research is to train a model capable of detecting and identifying instruments, and evaluate this model on different well-known datasets in the field of MIR. For this purpose, the OpenMIC-2018 dataset it’s used for training and three datasets to evaluate the model: MTG-Jamendo, NSynth and audio from live performances with separated instruments using Demucs. Accuracy will be one of the criteria used to assess the model’s performance. By addressing this issue, we hope to contribute to advancements in the field of MIR, enabling more personalized music recommendations through improved accuracy in recommendation systems. Additionally, we seek to provide insights to the MIR community, aiding in music analysis and related fields, in order to facilitate increasingly efficient applications.