CONEqNet: convolutional music equalizer network

Abstract The process of parametric equalization musical pieces seeks to highlight their qualities by cutting and/or stimulating certain frequencies. In this work, we present a neural model capable equalizing song according the genre that is being played at given moment. It normal (1) should adapt throughout and not always be same for whole song; (2) songs do belong specific may contain touches different genres. designed in called CONEqNet (convolutional music equalizer network), takes these aspects into account proposes adapting changes occur with possibility mixing nuances For training model, well-known GTzan dataset, which provides 1,000 fragments 30 seconds each, divided 10 genres, was used. paper will show proofs concept performance model.