The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.

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With the advent of deep learning, global tempo estimation accuracy has reached a new peak, which presents a great opportunity to evaluate our evaluation practices. In this article, we discuss presumed and actual applications, the pros and cons of commonly used metrics, and the suitability of popular datasets. To guide future research, we present results of a survey among domain experts that investigates today’s applications, their requirements, and the usefulness of currently employed metrics. To aid future evaluations, we present a public repository containing evaluation code as well as estimates by many different systems and different ground truths for popular datasets.@en

The International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies (MIRUM) at ACM Multimedia was proposed in order to gather experts from the Music and Multimedia Information Retrieval communities, as well as other neighboring fields, and to provide a high-profile platform for presenting current work on Music Information Retrieval with a strong focus on user-centered and multimodal approaches. Following a successful first edition at ACM Multimedia 2011, a second edition of MIRUM was held at ACM Multimedia 2012, which is the focus of this overview. After a description of the rationale and focus areas of the workshop, the accepted submissions and other program elements are summarized.

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The 1st International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies (MIRUM) at ACM Multimedia was proposed in order to gather experts from the Music and Multimedia Information Retrieval communities, as well as other neighboring fields. The workshop aims to provide a highprofile platform for presenting current work on Music Information Retrieval, with strong focus on user-centered and multimodal approaches. These focus areas are not only relevant to the Music Information Retrieval field, but equally recognized as emerging and relevant in the Multimedia domain. This way, a cross-disciplinary dialogue on open challenges can be initiated, facilitating new bridging opportunities and increased exchanges of expertise between communities. In this summary, we provide an overview of the 1st MIRUM workshop. After a description of the rationale and focus areas of the workshop, the accepted submissions and other program elements are summarized.@en

Music is a widely enjoyed content type, existing in many multi-faceted representations. With the digital information age, a lot of digitized music information has theoretically become available at the user's fingertips. However, the abundance of information is too large-scaled and too diverse to annotate, oversee and present in a consistent and human manner, motivating the development of automated Music Information Retrieval (Music-IR) techniques. In this paper, we encourage to consider music content beyond a monomodal audio signal and argue that Music-IR approaches with multimodal and user-centered strategies are necessary to serve reallife usage patterns and maintain and improve accessibility of digital music data. After discussing relevant existing work in these directions, we show that the field of Music-IR faces similar challenges as neighboring fields, and thus suggest opportunities for joint collaboration and mutual inspiration.@en