A review of the differences between musical genres and performing skills in different periods

Playing skill is the basic condition of musical instrument performance, and its level directly influences the timbre and musical expression of the work. In order to make music more appealing, it is essential to improve performance skills. Only by mastering the flute playing skills skillfully, can the work be more vocal, the timbre is soft, and the timbre is more unified, so that the thoughts, feelings and profound connotations of the musical works are more deeply rooted. The generality of music genre, that is to say, can fully show the common principles of music creation in a period of music and the criterion of musical aesthetics. The individuality of the music genre fully demonstrates the individual characteristics of the composer. In the music works in different periods of the musicians, the specific performance we can see the typical character of a music period, different features also can feel different with the composer.

Music Genre Classification Using DenseNet and Data Augmentation

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.

Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

Genres are one of the key features that categorize music based on specific series of patterns.However,the Arabic music content on the web is poorly defined into its genres,making the automatic classification of Arabic audio genres challenging.For this reason,in this research,our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres,which are:Eastern Takht,Rai,Muwashshah,the poem,and Mawwal,and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks(CNNs)architectures on Arabic music genres classification.In this work,to utilize CNNs to develop a practical classification system,the audio data is transformed into a visual representation(spectrogram)using Short Time Fast Fourier Transformation(STFT),then several audio features are extracted using Mel Frequency Cepstral Coefficients(MFCC).Performance evaluation of classifiers is measured with the accuracy score,time to build,and Matthew’s correlation coefficient(MCC).The concluded results demonstrated that AlexNet is considered among the topperforming five CNNs classifiers studied:LeNet5,AlexNet,VGG,ResNet-50,and LSTM-CNN,with an overall accuracy of 96%.

Influence of Social Determinants in Preference of Public Audience at Music Performances in the Republic of Macedonia

This paper displays the results of a survey of public music performances held during 2010 in Skopje, the capital of the Republic of Macedonia. Study of the audience of public musical events was limited only to the number of visitors. Field research included 653 musical events with a total of 545,340 visitors grouped into eight categories according to the preference by genres of music preference, age, status symbol, origin of the performers, space, and organizers. Our experience in monitoring public musical events through personal presence, the recorded materials, and the continuous monitoring of information from electronic and print media, enabled us to record events and to build some initial comments and assumptions about the structure of the audience and music preference. We have chosen to consider the impact of the sociological determinants on the preference of the audience in the public musical performances through the music genres, the age, the status symbol, the origin of the performers, the area of maintenance, and the organizers of musical events. According to the genre distribution, classical music events prevail, and the greatest numbers of visitors are registered at the pop rock concerts. The structure of the audience according to the age varies depending on the musical genre, so the widest age structure covers the events of pop rock music.