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

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

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