A Machine Learning Based Music Player by Detecting Emotions

The paper constitutes the implementation of Convolutional neural network for the emotion detection and thereby playing a song accordingly. Segregating the songs and playing them in accordance to one's mood could facilitate the music lover. Although there exist a lot of algorithms designed for it, the computation is not as expected. This paper eradicates such an issue by using CNN. In order to obtain minimal processing, multilayer perceptron are implemented by CNNs. In comparison to various algorithms for image classification, CNNs observed to have little-processing. This implies that the filters used in CNNs are advantageous when compared to traditional algorithm. The visualization of features directly can be less informative. Hence, we use the training procedure of back-propagation to activate the filters for better visualization. The multiple actions such as capturing, detecting the emotion and classifying the same can all be confined as one step through the use of CNN. The slow performances of the real-time approaches could be enhanced by regularizing the methods and by visualizing the hidden features. Hence the proposed approach could enhance the accuracy and the computation speed.