CONEqNet: convolutional music equalizer network

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

CONEqNet: convolutional music equalizer network Articles