Music Information Retrieval in Live Coding: A Theoretical Framework

The work presented in this article has been partly conducted while the first author was at Georgia Tech from 2015–2017 with the support of the School of Music, the Center for Music Technology and Women in Music Tech at Georgia Tech. Another part of this research has been conducted while the first author was at Queen Mary University of London from 2017–2019 with the support of the AudioCommons project, funded by the European Commission through the Horizon 2020 programme, research and innovation grant 688382. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Music information retrieval (MIR) has a great potential in musical live coding because it can help the musician–programmer to make musical decisions based on audio content analysis and explore new sonorities by means of MIR techniques. The use of real-time MIR techniques can be computationally demanding and thus they have been rarely used in live coding; when they have been used, it has been with a focus on low-level feature extraction. This article surveys and discusses the potential of MIR applied to live coding at a higher musical level. We propose a conceptual framework of three categories: (1) audio repurposing, (2) audio rewiring, and (3) audio remixing. We explored the three categories in live performance through an application programming interface library written in SuperCollider, MIRLC. We found that it is still a technical challenge to use high-level features in real time, yet using rhythmic and tonal properties (midlevel features) in combination with text-based information (e.g., tags) helps to achieve a closer perceptual level centered on pitch and rhythm when using MIR in live coding. We discuss challenges and future directions of utilizing MIR approaches in the computer music field

Piano Genie

We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user's input for the encoder output, and play the decoder's prediction each time the user presses a button. To improve the intuitiveness of Piano Genie's performance behavior, we impose musically meaningful constraints over the encoder's outputs.Comment: Published as a conference paper at ACM IUI 201

Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system

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

People-Powered Music: Using User-Generated Tags and Structure in Recommendations

Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches

When users generate music playlists: When words leave off, music begins?

Music systems that generate playlists are gaining increasing popularity, yet ways to select songs to be acceptable to users is still elusive. We present the results of an explorative study that focused on the language of musically untrained end users for playlist choices, in a variety of listening contexts. Our results indicate that there are a number of opportunities for playlist recommendation or retrieval systems, particularly by taking context into account