Reconhecimento de sons usando machine learning

Abstract

In recent years, the convergence between the music industry and machine learning techniques has generated increasing interest among both researchers and industry. One particularly fascinating area is voice and musical instrument recognition, where machine learning algorithms can be employed to automatically identify which instruments are present in an audio recording. This application has significant implications not only for the music industry, enabling efficient categorization of vast audio libraries, but also for computational music analysis, facilitating the extraction of musical features and the development of new tools and technologies for musicians and producers. However, voice and musical instrument recognition presents a number of unique challenges. The complexity of timbres, the overlap of multiple instruments in a recording, and the presence of background noise are just some of the difficulties faced by machine learning algorithms. Furthermore, the lack of standardized datasets and the need to train on a variety of musical styles represent additional obstacles to the development of robust and accurate musical instrument recognition systems. This paper aims to explore and analyze different approaches to musical instrument recognition using machine learning techniques, investigating their applications, challenges, and opportunities in the music industry and academic research.