Deep learning Binary/Multi classification for music's brainwave entrainment beats.

The last two decades have seen the emergence of a brand-new kind of music known as digital brain stimulant, also known as instrumental music or music without lyrics, which mostly comprises entrainment beats. While listening to it has the same ability to affect the brain as taking medication, it also has the risk of having a negative impact or encouraging unwanted behavior. This sparked the interest of a large number of studies in the psychological and physiological effects of music's brainwave entrainment beats on listeners. These studies started to categorize and examine how musical beats affected brainwave entrainment by looking at electroencephalogram (EEG) signals. Although this categorization represents a step forward for the early research efforts, it is constrained by the difficulty of having each musical track and conducting EEG tests on humans exposed to distortion due to noise in order to determine its influence. The work proposed in this article continues to explore this topic but in a novel, simple, accurate, and reliable categorization procedure based on the music signal elements themselves rather than dependent on EEG. VGGish and YAMNET based transfer deep learning models, are tuned to handle a straightforward, accurate real-time detector for the existence of the music beats inside music files with accuracy of 98.5 and 98.4, respectively. Despite the fact that they yield results that are equivalent, the YAMNET model is more suited for use with mobile devices due to its low power consumption and low latency. The article also proposes modified version of VGGish and YAMNET binary classifying models called BW-VGGish and BW-YAMNET respectively. The modification was to turn the binary classification into multi-classification. These multi-classifiers handle the classification of the influence of music beats (five different brain waves) on human brainwave entrainment with average accuracy of 94.5% and 94.5%, respectively. Since there was a lack of datasets addressing this kind of music, two datasets, the Brainwave Entrainment Beats (BWEB) dataset and the Brainwave Music Manipulation (BWMM) dataset, were generated for classification training and testing. The re-testing on a sample of music files that have their impact on brain waves (with their EEG) in an earlier study is done to strengthen the validity of the proposed work and to overcome the potential limitation of utilizing a music dataset that is not proved with its EEG. The success of the suggested models was demonstrated.