Music Genre Classification using Neural Networks

Abstract

In recent years, the complexity of making music has lessened, resulting in many individuals making music and submitting it to streaming media. Because of the huge music streaming media, people are spending a lot of time seeking for certain songs. As a result, the capacity to swiftly categorise music genres has become increasingly important. As machine learning and deep learning technologies progress, convolutional neural networks (CNN) are being employed in several fields, and several CNN-based versions have emerged one after the other. Traditional music genre classification necessitates professional abilities to manually extract features from time series data. We developed a music genre categorization model using CNN's audio advantages and features to save users time while searching for different types of music. During the pre-processing, Librosa is used to convert the original audio files into Mel spectrums.

 

The Mel spectrum is transformed and supplied into the suggested CNN model for training. On the GTZAN dataset, the 10 classifiers' decisions are subjected to a majority vote, with an average accuracy of 84 percent. Music genre categorization using neural networks (NNs) has seen some modest success in recent years. The success of song libraries, machine learning techniques, input formats, and the types of NNs utilised has all been mixed. This article looks at some of the machine learning approaches utilised in this sector. It also involves research on musical genre classification. Images of spectrograms produced from time slices of songs are fed into a neural network (NN) to classify the songs into different musical genres.