Text Data Analysis in Chinese Folk Music with Effective Clustering Model toward Feature Identification of Inheritance

Folk music based on big data analysis can provide valuable insights into the history, culture, and evolution of traditional music. By understanding the historical and cultural contexts of folk music, one better appreciate its value and contribute to its continued development and inheritance. Big data analysis can help identify patterns and trends in the performance, distribution, and reception of folk music across time and space. In this paper designed a Weighted Clustering Euclidean Feature (WCEF) model to evaluate folk music on the development of inheritance. Initially, the text data is extracted from folk music for the estimation of features in the big data analysis. Secondly, the WCEF model uses a clustering model for a subset of the folk music dataset with Weighted Non-Negative Matrix Factorization (WNMF). With the clustered model feature extraction is computed with Named Entity Recognition (NER). The NER model uses the Euclidean distance estimation for the computation of features in the folk data analysis. Finally, the WCEF model uses the deep learning model for the classification of inheritance in folk music. The experimental analysis stated that the WCEF model effectively classifies the folk music words and their contribution to inheritance

On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres

This paper focuses on the modeling of musical melodies as networks. Notes of a melody can be treated as nodes of a network. Connections are created whenever notes are played in sequence. We analyze some main tracks coming from different music genres, with melodies played using different musical instruments. We find out that the considered networks are, in general, scale free networks and exhibit the small world property. We measure the main metrics and assess whether these networks can be considered as formed by sub-communities. Outcomes confirm that peculiar features of the tracks can be extracted from this analysis methodology. This approach can have an impact in several multimedia applications such as music didactics, multimedia entertainment, and digital music generation.Comment: accepted to Multimedia Tools and Applications, Springe

Methodological contributions by means of machine learning methods for automatic music generation and classification

189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere

Graph based representation of the music symbolic level. A music information retrieval application

In this work, a new music symbolic level representation system is described. It has been tested in two information retrieval tasks concerning similarity between segments of music and genre detection of a given segment. It could include both harmonic and contrapuntal informations. Moreover, a new large dataset consisting of more than 5000 leadsheets is presented, with meta informations taken from different web databases, including author information, year of first performance, lyrics, genre, etc.ope

Measuring musics:Notes on modes, motifs, and melodies

This dissertation develops computational methods to measure properties of musical traditions, with the aim of comparing them. It analyzes sheet music from a range of traditions, to which end two corpora of Western plainchant are introduced (Cantus Corpus and GregoBase Corpus). These corpora are used to confirm the melodic arch hypothesis, explore regularity in antiphon-differentia connections, and compose artificial chant using a recurrent neural language model. The central chant study, however, proposes a distributional approach to mode classification that can still determine mode fairly accurately even when all pitch information has been discarded. However, this seems to work best when the chants are segmented into ‘natural units’ corresponding to textual units such as syllables and words. Breaking down music into smaller units, or motifs, is the second theme in this dissertation. It is shown how rhythmic motifs can be used to effectively visualize rhythmic data, from music and animal vocalizations, in a rhythm triangle, an idea that is also extended to melodic data. The third theme concerns the shapes of melodies. The dissertation introduces Cosine Contours: a continuous representation for melodic contour, motivated by the observation that the principal components of melodic datasets approximate cosines. A second study on contour suggests that it should indeed be considered a continuous phenomenon, unlike several previous studies, as no evidence is found that contours cluster in distinct types. The dissertation ends with a case-study that applies a formal analysis to the ‘formal’ music of Arvo Pärt by reconstructing almost the entire score of ‘Summa’ using formal procedures

Using Generic Summarization to Improve Music Information Retrieval Tasks

In order to satisfy processing time constraints, many MIR tasks process only a segment of the whole music signal. This practice may lead to decreasing performance, since the most important information for the tasks may not be in those processed segments. In this paper, we leverage generic summarization algorithms, previously applied to text and speech summarization, to summarize items in music datasets. These algorithms build summaries, that are both concise and diverse, by selecting appropriate segments from the input signal which makes them good candidates to summarize music as well. We evaluate the summarization process on binary and multiclass music genre classification tasks, by comparing the performance obtained using summarized datasets against the performances obtained using continuous segments (which is the traditional method used for addressing the previously mentioned time constraints) and full songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA, MMR, and a Support Sets-based Centrality model improve classification performance when compared to selected 30-second baselines. We also show that summarized datasets lead to a classification performance whose difference is not statistically significant from using full songs. Furthermore, we make an argument stating the advantages of sharing summarized datasets for future MIR research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processin

Learning a feature space for similarity in world music

In this study we investigate computational methods for assessing music similarity in world music styles. We use state-of-the-art audio features to describe musical content in world music recordings. Our music collection is a subset of the Smithsonian Folkways Recordings with audio examples from 31 countries from around the world. Using supervised and unsupervised dimensionality reduction techniques we learn feature representations for music similarity. We evaluate how well music styles separate in this learned space with a classification experiment. We obtained moderate performance classifying the recordings by country. Analysis of misclassifications revealed cases of geographical or cultural proximity. We further evaluate the learned space by detecting outliers, i.e. identifying recordings that stand out in the collection. We use a data mining technique based on Mahalanobis distances to detect outliers and perform a listening experiment in the ‘odd one out’ style to evaluate our findings. We are able to detect, amongst others, recordings of non-musical content as outliers as well as music with distinct timbral and harmonic content. The listening experiment reveals moderate agreement between subjects’ ratings and our outlier estimation