What makes us musical animals
We are all born with a predisposition for music, a predisposition that develops spontaneously and is refined by listening to music. Nearly everyone possesses the musical skills essential to experiencing and appreciating music. Think of “relative pitch,” recognizing a melody separately from the exact pitch or tempo at which it is sung, and “beat perception,” hearing regularity in a varying rhythm. Research shows that all humans possess the trait of musicality. We are a musical species — but are we the only musical species? Can there be musical machines? In his presentation, Henkjan Honing embarks upon the quest to discover the cognitive and biological mechanisms that underpin musicality.
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Henkjan Honing is a professor of Music Cognition at both the Faculty of Humanities and the Faculty of Science of the University of Amsterdam (UvA). He studies what musicality is or can be and to what extent human beings share musicality with other animals. His aim is to define the cognitive and biological mechanisms that underpin musicality. In addition to a research agenda (The Origins of Musicality, 2018, MIT Press), Honing has published several books for the general public, including the English-language publications Musical Cognition and The Evolving Animal Orchestra. Honing’s books and lectures are popular with a broad audience and are appreciated both inside and outside the scientific world. |
MIR redux: Knowledge and real-world challenges, and new interdisciplinary futures
How can MIR refresh itself and its endeavors, scholarly and real world? I speak as an outsider, and it is foolhardy to advise scientist colleagues whose methodologies one would be hard pressed to follow! Nonetheless, my question points in two directions: first, to two areas of auto-critique that have emerged within the MIR community – to do with the status of the knowledge produced, and ethical and social concerns. One theme that unites them is interdisciplinarity: how MIR would gain from closer dialogues with musicology, ethnomusicology, music sociology, and science and technology studies in music. Second, the ‘refresh’ might address MIR’s pursuit of scientific research oriented to technological innovation, itself invariably tied to the drive for economic growth. The burgeoning criticisms of the FAANG corporations and attendant concerns about sustainable economies remind us of the urgent need for other values to guide science and engineering. We might ask: what would computational genre recognition or music recommendation look like if, under public-cultural or non-profit imperatives, the incentives driving them aimed to optimise imaginative and cultural self- and/or group development, adhering not to a logic of ‘similarity’ but diversity, or explored the socio-musical potentials of music discovery, linked to goals of human flourishing (Nussbaum 2003, Hesmondhalgh 2013)? The time is ripe for intensive and sustained interdisciplinary engagements in ways previously unseen. My keynote ends by inviting action: a think tank to take this forward.
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Georgina Born OBE FBA is Professor of Music and Anthropology at the University of Oxford, was the bass player with Henry Cow in the late 70s, played improvised cello in the 80s, wrote an ethnography of IRCAM in the 90s and an ethnography of the BBC in the 2000s, and is a leading interdisciplinary scholar writing on the mediation of music, especially its social forms. She ran an ERC-funded research group (2010-15) studying ethnographically how digitization and the internet have affected music worldwide. |
Music as Investigation
The music information retrieval landscape has changed dramatically in the past two decades since ISMIR’s inception. Music and user data exist at web scale and systems and algorithms have evolved to take advantage of it. Yet there remain classes of problems and information needs for which scale data will never be available. The early ISMIR community, perhaps if only out of necessity, responded to such challenges by adopting various mindsets: exploratory, investigatory, niche. The benefits of these mindsets extend far beyond music into other individual-scale, task-oriented domains such as law, where popularity and prevalence do not provide easily-distillable answers. Do the benefits run both ways?
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Jeremy Pickens is a Principal Data Scientist at OpenText (né Catalyst Repository Systems), a leader in enterprise software and legal technology. His research in information retrieval has spanned a number of domains from music to images and video to various legal applications. The common thread among these disparate areas has been an emphasis on recall-oriented, comprehensive, and holistic views of relevance. Jeremy is a pioneer in the field of collaborative exploratory search, a form of information seeking in which a group of people who share a complex information need actively collaborate to achieve it. His ongoing research focuses on methods for continuous learning. Jeremy holds a PhD in Computer Science from the University of Massachusetts, Amherst, Center for Intelligent Information Retrieval. He conducted his post-doctoral work at King’s College, London. Before joining Catalyst and OpenText, he spent five years as a research scientist at FX Palo Alto Lab, Inc. He was also a member of many of the early (2000-2005) ISMIR organizing committees. |
Music and algorithmic responsibilities in practice
Music is deeply personal, and influences our moods and motivation. Music creates communities, shapes subcultures, and powers an enormous industry. What music is available, what can be found, what is recommended, matters. Who gets to learn, to play, to record, matters.
And, crucially, how the music recommendation and retrieval community defines success determines who gets amplified.
We're at a moment in tech where, alongside its successes, machine learning's failures and biases have gained much attention. There is an overwhelming number of calls-to-action but still relatively few standard practices for industry practitioners. This means we have a responsibility, especially as an ISMIR community. But what does that mean, practically?
This talk will outline challenges encountered in practice and at scale, specific to music streaming. We’ll briefly travel through time, and take you from early 1900’s magic lantern slides’ music promotion to the current zeitgeist where new guidelines for algorithmic accountability are clamoring for attention themselves. We’ll discuss how new UIs (like voice) can make certain creators inaccessible, female creators’ representation in streaming, and optimizing for more than just engagement. We’ll share technical and organizational lessons learned, pitfalls, and tensions in assessing decisions’ potential impact.
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Henriette Cramer is a principal researcher at Spotify Research, and product manages Spotify’s Algorithmic Responsibility effort. She is particularly interested in the impact that teams' design, data and organizational decisions have on algorithmic outcomes. Prior, she set up Spotify’s ‘human side of Machine Learning’ Hai lab, and led data research for Spotify's voice platform. She has worked on recommendations, ad quality, and conversational interactions at Yahoo, and on location-based data, perceptions of place, and human-robot interaction at the Swedish Institute of Computer Science. She holds a PhD from the University of Amsterdam focused on people’s responses to autonomous systems. More at: http://henriettecramer.com |