From listening to curating: Anthropological curatorship toward music playlist practices

Currently, the curatorial approach is expanding its influence beyond the limitations of museums and art galleries, transforming the experiences and significance of our daily existence. That is embodied, for example, in the curatorial practices of music playlists. In making a music playlist there must be a sound curation process where the songs to listen to are selected and included in the music playlist. Accordingly, this study aims to describe how users of analog (mixtapes) and digital (Spotify playlist) music playlists conduct their music curation process and perceive the relationships behind them. The curation process is analyzed using curatorial and anthropological frameworks, which also attempt to provide a fascinating background on preference formation and the curation process. Qualitative data was collected from a virtual ethnographic approach, with observations and unstructured-semi-structured interviews—offline and online—of 19 informants aged 21–55 living in Yogyakarta and Bandung and a literature review. The data obtained through this study focuses on two outcomes. First, sociocultural circumstances provide the basis for preference formation in selecting, curating, and making music playlists. Second, the particulars of each user's music playlist are related to self-discovery and individual identity. As a result, sociocultural circumstances persuade music playlist users' habits that attach to the curation process

Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions *

ABSTRACT We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists

Feature-combination hybrid recommender systems for automated music playlist continuation

Music recommender systems have become a key technology to support the interaction of users with the increasingly larger music catalogs of on-line music streaming services, on-line music shops, and personal devices. An important task in music recommender systems is the automated continuation of music playlists, that enables the recommendation of music streams adapting to given (possibly short) listening sessions. Previous works have shown that applying collaborative filtering to collections of curated music playlists reveals underlying playlist-song co-occurrence patterns that are useful to predict playlist continuations. However, most music collections exhibit a pronounced long-tailed distribution. The majority of songs occur only in few playlists and, as a consequence, they are poorly represented by collaborative filtering. We introduce two feature-combination hybrid recommender systems that extend collaborative filtering by integrating the collaborative information encoded in curated music playlists with any type of song feature vector representation. We conduct off-line experiments to assess the performance of the proposed systems to recover withheld playlist continuations, and we compare them to competitive pure and hybrid collaborative filtering baselines. The results of the experiments indicate that the introduced feature-combination hybrid recommender systems can more accurately predict fitting playlist continuations as a result of their improved representation of songs occurring in few playlists(VLID)328909

Controlling attention and visibility in the record industries

Master's thesis in music management MU501 - University of Agder 2016The background for the thesis in hand is the changed environment of the record industries due to digitalization. Various democratizing and decentralizing expectations on digitalization exist in the academics. Research have been conducted towards the changed structures of the record industries, leaving an image of a decentralized supply chain. This opens possibilities for independent labels and artists to circumvent the traditional intermediaries. Contrasting, the record industries' market shares have not changed significantly in the past decades. It is questionable if the digitalization has led to democratizing and decentralizing structures and if it can fulfill those optimistic views in general. The purpose of this work is to make the shifted structure and power relations of the record industries visible. By applying an exploratory research approach and a scoping literature review as a method, the structure of the record industries before and after the digitalization will be depicted. Thereby, Porter’s five competitive forces and the supply chain model will help to structure the pre- and post-digitalized states of the record industries. After gaining significant knowledge about the record industries’ structure, immanent power relations will be analyzed. The present study focuses on the record industries’ supply chain. This model will help to identify power relations between different intermediaries. Those enable the actors of the record industries to set up bottlenecks within the production flow of recorded music, which provides them with control over the supply chain. This paper will provide an innovative perspective on the nature and location of a new bottleneck within the record industries’ supply chain. This bottleneck occurred due to the disruption of the supply chain and re-intermediation, which is based on power relations between the different actors. With that, this work reveals a lack of current research and provides suggestions for future studies

Deep Neural Networks for Music Tagging

PhDIn this thesis, I present my hypothesis, experiment results, and discussion that are related to various aspects of deep neural networks for music tagging. Music tagging is a task to automatically predict the suitable semantic label when music is provided. Generally speaking, the input of music tagging systems can be any entity that constitutes music, e.g., audio content, lyrics, or metadata, but only the audio content is considered in this thesis. My hypothesis is that we can fi nd effective deep learning practices for the task of music tagging task that improves the classi fication performance. As a computational model to realise a music tagging system, I use deep neural networks. Combined with the research problem, the scope of this thesis is the understanding, interpretation, optimisation, and application of deep neural networks in the context of music tagging systems. The ultimate goal of this thesis is to provide insight that can help to improve deep learning-based music tagging systems. There are many smaller goals in this regard. Since using deep neural networks is a data-driven approach, it is crucial to understand the dataset. Selecting and designing a better architecture is the next topic to discuss. Since the tagging is done with audio input, preprocessing the audio signal becomes one of the important research topics. After building (or training) a music tagging system, fi nding a suitable way to re-use it for other music information retrieval tasks is a compelling topic, in addition to interpreting the trained system. The evidence presented in the thesis supports that deep neural networks are powerful and credible methods for building a music tagging system