It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation,distribution,and enjoyment of musical works have undergone h...It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation,distribution,and enjoyment of musical works have undergone huge changes.As the number ofmusic products increases daily and themusic genres are extremely rich,storing,classifying,and searching these works manually becomes difficult,if not impossible.Automatic classification ofmusical genres will contribute to making this possible.The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive(FMA)data set.For Small FMA,it is more efficient to augment the data by generating an echo rather than pitch shifting.The research results show that the DenseNet121 model and data augmentation methods,such as noise addition and echo generation,have a classification accuracy of 98.97%for the Small FMA data set,while this data set lowered the sampling frequency to 16000 Hz.The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.展开更多
基金The authors received the research fun T2022-CN-006 for this study.
文摘It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation,distribution,and enjoyment of musical works have undergone huge changes.As the number ofmusic products increases daily and themusic genres are extremely rich,storing,classifying,and searching these works manually becomes difficult,if not impossible.Automatic classification ofmusical genres will contribute to making this possible.The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive(FMA)data set.For Small FMA,it is more efficient to augment the data by generating an echo rather than pitch shifting.The research results show that the DenseNet121 model and data augmentation methods,such as noise addition and echo generation,have a classification accuracy of 98.97%for the Small FMA data set,while this data set lowered the sampling frequency to 16000 Hz.The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.
