Sonification of Samba dance using periodic pattern analysis

In this study we focus on the sonification of Samba dance, using a multi-modal analysis-by-synthesis approach. In the analysis we use periodic pattern analysis to decompose the Samba dance movements into basic movement gestures along the music’s metric layers. In the synthesis we start from the basic movement gestures and extract peaks and valleys, which we use as basic material for the sonification. This leads to a matrix of repetitive dance gestures from which we select the proper cues that trigger samples of a Samba ensemble. The straightforward sonification procedure suggests that Samba rhythms may be mirrored in choreographic forms or vice-versa

A computational study on outliers in world music

The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country

Evaluating Collaborative Filtering Algorithms for Music Recommendations on Chinese Music Data

In this thesis, I explored Collaborative Filtering algorithms used in music recommendation tasks in the Music Information Retrieval field. To find out if those CF algorithms work on Chinese music data, I developed a new dataset from the mainstream Chinese music streaming platform NetEase Could Music, and compared the performance of a series of Memory-based and Model-based collaborative filtering algorithms on our dataset. Our experimental results prove that these CF algorithms aiming at users’ information are effective on our dataset, and they have the predictive ability of music recommendation tasks on Chinese music data. In general, Model-based algorithms perform better than Memory-based algorithms. Within them, the SVD++ algorithm from Matrix Factorization-based methods reaches the best overall accuracy.Bachelor of Scienc