Papers by Asher T Chodos
Journal of Popular Music Studies, 2020
The growing field of “critical algorithm studies” often addresses the cultural consequences of ma... more The growing field of “critical algorithm studies” often addresses the cultural consequences of machine learning, but it has ignored music. The result is that we inhabit a musical culture intimately bound up with various forms of algorithmic mediation, personalization, and “surveillance capitalism” that has largely escaped critical attention. But the issue of algorithmic mediation in music should matter to us, if music matters to us at all. This article lays the groundwork for such critical attention by looking at one major musical application of machine learning: Spotify’s automated music recommendation system. In particular, it takes for granted that any musical recommendation – whether made by a person or an algorithm – must necessarily imply a tacit theory of musical meaning. In the case of Spotify, we can make certain claims about that theory, but there are also limits to what we can know about it. Both things – the deductions and the limitations – prove valuable for a critique ...
Current Musicology, Nov 15, 2019
In 2019, my last year of graduate school, I taught the department's undergraduate history of ... more In 2019, my last year of graduate school, I taught the department's undergraduate history of Hip Hop. This essay reflects on some of the many issues this course raises.
Author(s): Chodos, Asher Tobin | Advisor(s): Borgo, David; Steiger, Rand | Abstract: Music has no... more Author(s): Chodos, Asher Tobin | Advisor(s): Borgo, David; Steiger, Rand | Abstract: Music has not been exempt from the so-called "curatorial turn" that is visible in so many parts of our culture -- the turn, that is, toward machine learning to help consumers sort through the glut of media with which we are all confronted today. Automated music recommendations, in fact, are probably the single biggest driver of the music industry's recovery from the crisis it faced at the beginning of the 20th century. Nobody knows what shape the music industry will take if and when this recovery is complete, but it seems certain that automated curation will be at its center. This fact represents a confrontation of the human faculty of aesthetic judgment and machine learning at a scale the world has never seen. The issue of automated music recommendation raises many of the philosophical problems familiar from the critique of technology in culture. It does so, moreover, in a way that of...
Jazz Perspectives
Today, the blues scale is familiar to most people who have studied music formally. Although there... more Today, the blues scale is familiar to most people who have studied music formally. Although there has been since 1967 a broad consensus as to what that scale is, for the first half of the twentieth century there was actually a good deal of disagreement on that point. A close look at the history of the blues scale reveals that disagreements over its content are bound up with widespread ambiguity concerning its epistemological status. This paper seeks to illuminate that epistemological confusion, proceeding in two ways: first, it historicizes today’s blues scale by laying out the main blues scales proposed between 1938 and 1967, attending to the role these scales played in the institutionalization of jazz education; second, it demonstrates that these scales differ not just in content and attitude but also in epistemological orientation. Because of its social overtones and political implications, disagreements over the nature (and even existence) of the blues scale have frequently been heated. This paper argues that these disagreements derive in part from a persistent epistemological confusion that has characterized much of the discourse surrounding this musical idea.
Jazz Perspectives
Today, the blues scale is familiar to most people who have studied music formally. Although there... more Today, the blues scale is familiar to most people who have studied music formally. Although there has been since 1967 a broad consensus as to what that scale is, for the first half of the twentieth century there was actually a good deal of disagreement on that point. A close look at the history of the blues scale reveals that disagreements over its content are bound up with widespread ambiguity concerning its epistemological status. This paper seeks to illuminate that epistemological confusion, proceeding in two ways: first, it historicizes today’s blues scale by laying out the main blues scales proposed between 1938 and 1967, attending to the role these scales played in the institutionalization of jazz education; second, it demonstrates that these scales differ not just in content and attitude but also in epistemological orientation. Because of its social overtones and political implications, disagreements over the nature (and even existence) of the blues scale have frequently been heated. This paper argues that these disagreements derive in part from a persistent epistemological confusion that has characterized much of the discourse surrounding this musical idea.
Current Musicology, 2019
In 2019, my last year of graduate school, I taught the department’s undergraduate history of Hip ... more In 2019, my last year of graduate school, I taught the department’s undergraduate history of Hip Hop. This essay reflects on some of the many issues this course raises.
INSAM Journal of Contemporary Music, Art and Technology, 2019
The growing field of “critical algorithm studies” often addresses
the cultural consequences of ma... more The growing field of “critical algorithm studies” often addresses
the cultural consequences of machine learning, but it has ignored music.
The result is that we inhabit a musical culture intimately bound up with
various forms of algorithmic mediation, personalization, and “surveillance
capitalism” that has largely escaped critical attention. But the issue of
algorithmic mediation in music should matter to us, if music matters to us
at all. This article lays the groundwork for such critical attention by looking
at one major musical application of machine learning: Spotify’s automated
music recommendation system. In particular, it takes for granted that any
musical recommendation – whether made by a person or an algorithm – must necessarily imply a tacit theory of musical meaning. In the case of Spotify, we can make certain claims about that theory, but there are also limits to what we can know about it. Both things – the deductions and the limitations – prove valuable for a critique of automated music curation in general.
Jazz Perspectives, 2019
Today, the blues scale is familiar to most people who have studied music formally. Although there... more Today, the blues scale is familiar to most people who have studied music formally. Although there has been since 1967 a broad consensus as to what that scale is, for the first half of the twentieth century there was actually a good deal of disagreement on that point. A close look at the history of the blues scale reveals that disagreements over its content are bound up with widespread ambiguity concerning its epistemological status. This paper seeks to illuminate that epistemological confusion, proceeding in two ways: first, it historicizes today’s blues scale by laying out the main blues scales proposed between 1938 and 1967, attending to the role these scales played in the institutionalization of jazz education; second, it demonstrates that these scales differ not just in content and attitude but also in epistemological orientation. Because of its social overtones and political implications, disagreements over the nature (and even existence) of the blues scale have frequently been heated. This paper argues that these disagreements derive in part from a persistent epistemological confusion that has characterized much of the discourse surrounding this musical idea.
Do the Math, 2019
Guest post on the history of the Blues Scale published to Ethan Iverson's blog, "Do the Math."
Book Reviews by Asher T Chodos
The Journal of Popular Music Studies, 2020
Critical Studies in Improvisation // Études critiques en improvisation, 2018
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Papers by Asher T Chodos
the cultural consequences of machine learning, but it has ignored music.
The result is that we inhabit a musical culture intimately bound up with
various forms of algorithmic mediation, personalization, and “surveillance
capitalism” that has largely escaped critical attention. But the issue of
algorithmic mediation in music should matter to us, if music matters to us
at all. This article lays the groundwork for such critical attention by looking
at one major musical application of machine learning: Spotify’s automated
music recommendation system. In particular, it takes for granted that any
musical recommendation – whether made by a person or an algorithm – must necessarily imply a tacit theory of musical meaning. In the case of Spotify, we can make certain claims about that theory, but there are also limits to what we can know about it. Both things – the deductions and the limitations – prove valuable for a critique of automated music curation in general.
Book Reviews by Asher T Chodos
the cultural consequences of machine learning, but it has ignored music.
The result is that we inhabit a musical culture intimately bound up with
various forms of algorithmic mediation, personalization, and “surveillance
capitalism” that has largely escaped critical attention. But the issue of
algorithmic mediation in music should matter to us, if music matters to us
at all. This article lays the groundwork for such critical attention by looking
at one major musical application of machine learning: Spotify’s automated
music recommendation system. In particular, it takes for granted that any
musical recommendation – whether made by a person or an algorithm – must necessarily imply a tacit theory of musical meaning. In the case of Spotify, we can make certain claims about that theory, but there are also limits to what we can know about it. Both things – the deductions and the limitations – prove valuable for a critique of automated music curation in general.