“This AI will heat up any club”: Reggaetón and the Rise of the Cyborg Genre
This series listens to the political, gendered, queer(ed), racial engagements and class entanglements involved in proclaiming out loud: La-TIN-x. ChI-ca-NA. La-TI-ne. ChI-ca-n-@. Xi-can-x. Funded by an Andrew W. Mellon Foundation as part of the Crossing Latinidades Humanities Research Initiative, the Latinx Sound Cultures Studies Working Group critically considers the role of sound and listening in our formation as political subjects. Through both a comparative and cross-regional lens, we invite Latinx Sound Scholars to join us as we dialogue about our place within the larger fields of Chicanx/Latinx Studies and Sound Studies. We are delighted to publish our initial musings with Sounding Out!, a forum that has long prioritized sound from a queered, racial, working-class and “always-from-below” epistemological standpoint. —Ed. Dolores Inés Casillas
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Busco la colaboración universal donde todos los Benitos puedan llegar a ser Bad Bunny. –FlowGPT, TikTok
In November of 2023, the reggaetón song “DEMO #5: NostalgIA” went viral on various digital platforms, particularly TikTok. The track, posted by user FlowGPT, makes use of artificial intelligence (Inteligencia Artificial) to imitate the voices of Justin Bieber, Bad Bunny, and Daddy Yankee. The song begins with a melody reminiscent of Justin Bieber’s 2015 pop hit “Sorry.” Soon, reggaetón’s characteristic boom-ch-boom-chick drumbeat drops, and the voices of the three artists come together to form a carefully crafted, unprecedented crossover.
Bad Bunny’s catchy verse “sal que te paso a buscar” quickly inundated TikTok feeds as users began to post videos of themselves dancing or lip-syncing to the song. The song was not only very good but it also successfully replicated these artists– their voices, their style, their vibe. Soon, the song exited the bounds of the digital and began to be played in clubs across Latin America, marking a thought-provoking novelty in the usual repertoire of reggaetón hits. In line with the current anxieties around generative AI, the song quickly generated public controversy. Only a few weeks after its release, ‘nostalgIA’ was taken down from most digital platforms.
The mind behind FlowGPT is Chilean producer Maury Senpai, who in a series of TikTok responses explained his mission of creative democratization in a genre that has been historically exclusive of certain creators. In one video, FlowGPT encourages listeners to contemplate the potential of this “algorithm” to allow songs by lesser-known artists and producers to reach the ears of many listeners, by replicating the voices of well-known singers. Maury Senpai’s production process involved lyric writing, extensive study of the singers’ vocals, and the Kits.ai tool.
Therefore, contrary to FlowGPT’s robotic brand, ‘nostalgIA’ was the product of careful collaboration between human and machine– or, what Ross Cole calls “cyborg creativity.” This hybridization enmeshes the artist and the listener, allowing diverse creators their creative desires. Cyborg creativity, of course, is not an inherent result of GenAI’s advent. Instead, I argue that reggaetón has long been embedded in a tradition of musical imitation and a deep reliance on technological tools, which in turn challenges popular concerns about machine-human artistic collaboration.
Many creators worry that GenAI will co-opt a practice that for a long time has been regarded as strictly human. GenAI’s reliance on pre-existing data threatens to hide the labor of artists who contributed to the model’s output. We may also add the inherent biases present in training data. Pasquinelli and Joler propose that the question “Can AI be creative?” be reformulated as “Is machine learning able to create works that are not imitations of the past?” Machine learning models detect patterns and styles in training data and then generate “random improvisation” within this data. Therefore, GenAI tools are not autonomous creative actors but often operate with generous human intervention that trains, monitors, and disseminates the products of these models.
The inability to define GenAI tools as inherently creative on their own does not mean they can’t be valuable for artists seeking to experiment in their work. Hearkening back to Donna Haraway’s concept of the cyborg, Ross Cole argues that
Such [AI] music is in fact a species of hybrid creativity predicated on the enmeshing of people and computers (…) We might, then, begin to see AI not as a threat to subjective expression, but another facet of music’s inherent sociality.
Many authors agree that unoriginal content—works that are essentially reshufflings of existing material—cannot be considered legitimate art. However, an examination of the history of the reggaetón genre invites us to question this idea. In “From Música Negra to Reggaetón Latino,” Wayne Marshall explains how the genre emerged from simultaneous and mutually-reinforcing processes in Panamá, Puerto Rico, and New York, where artists brought together elements of dancehall, reggae, and American hip hop. Towards the turn of the millennium, the genre’s incorporation of diverse musical elements and the availability of digital tools for production favored its commercialization across Latin America and the United States.
The imitation of previous artists has been embedded in the fabric of reggaetón from a very early stage. Some of the earliest examples of reggaetón were in fact Spanish lyrics placed over Jamaican dancehall riddims— instrumental tracks with characteristic melodies. When Spanish-speaking artists began to draw from dancehall, they used these same riddims in their songs, and continue to do so today. A notable example of this pattern is the Bam Bam riddim, which is famously used in the song “Murder She Wrote” by Chaka Demus & Pliers (1992).
This riddim made its way into several reggaetón hits, such as “El Taxi” by Osmani García, Pitbull, and Sensato (2015).
We may also observe reggaetón’s tradition of imitation in frequent references to “old school” artists by the “new school,” through beat sampling, remixes, and features. We see this in Karol G’s recent hit “GATÚBELA,” where she collaborates with Maldy, former member of the iconic Plan B duo.
Reggaetón’s deeply rooted tradition of “tribute-paying” also ties into its differentiation from other genres. As the genre grew in commercial value, perhaps to avoid copyright issues, producers cut down on their direct references to dancehall and instead favored synthesized backings. Marshall quotes DJ El Niño in saying that around the mid-90s, people began to use the term reggaetón to refer to “original beats” that did not solely rely on riddims but also employed synthesizer and sequencer software. In particular, the program Fruity Loops, initially launched in 1997, with “preset” sounds and effects provided producers with a wider set of possibilities for sonic innovation in the genre.
The influence of technology on music does not stop at its production but also seeps into its socialization. Today, listeners increasingly engage with music through AI-generated content. Ironically, following the release of Bad Bunny’s latest album, listeners expressed their discontent through AI-generated memes of his voice. One of the most viral ones consisted of Bad Bunny’s voice singing “en el McDonald’s no venden donas.”
The clip, originally sung by user Don Pollo, was modified using AI to sound like Bad Bunny, and then combined with reggaetón beats and the Bam Bam riddim. Many users referred to this sound as a representation of the light-heartedness they saw lacking in the artist’s new album. While Un Verano Sin Ti (2022) stood out as an upbeat summer album that addressed social issues such as U.S. imperialism and machismo, Nadie Sabe lo que va a Pasar Mañana (2023) consisted mostly of tiraderas or disses against other artists and left some listeners disappointed. In a 2018 post for SO!, Michael S. O’Brien speaks of this sonic meme phenomenon, where a sound and its repetition come to encapsulate collective discontent.
Another notorious case of AI-generated covers targets recent phenomenon Young Miko. As one of the first openly queer artists to break into the urban Latin mainstream, Young Miko filled a long-standing gap in the genre—the need for lyrics sung by a woman to another woman. Her distinctive voice has also been used in viral AI covers of songs such as “La Jeepeta,” and “LALA,” originally sung by male artists. To map Young Miko’s voice over reggaetón songs that advance hypermasculinity– through either a love for Jeeps or not-so-subtle oral sex– represents a creative reclamation of desire where the agent is no longer a man, but a woman. Jay Jolles writes of TikTok’s modifications to music production, namely the prioritization of viral success. The case of AI-generated reggaetón covers demonstrates how catchy reinterpretations of an artist’s work can offer listeners a chance to influence the music they enjoy, allowing them to shape it to their own tastes.
Examining the history of musical imitation and digital innovation in reggaetón expands the bounds of artistry as defined by GenAI theorists. In the conventions of the TikTok platform, listeners have found a way to participate in the artistry of imitation that has long defined the genre. The case of FlowGPT, along with the overwhelmingly positive reception of “nostalgIA,” point towards a future where the boundaries between the listener and the artist are blurred, and where technology and digital spaces are the platforms that allow for an enhanced cyborg creativity to take place.
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Featured Image: Screenshot from ““en el McDonald’s no venden donas.” Taken by SO!
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Laurisa Sastoque is a Colombian scholar of digital humanities, history, and storytelling. She works as a Digital Preservation Training Officer at the University of Southampton, where she collaborates with the Digital Humanities Team to promote best practices in digital preservation across Galleries/Gardens, Libraries, Archives, and Museums (GLAM), and other sectors. She completed an MPhil in Digital Humanities from the University of Cambridge as a Gates Cambridge scholar. She holds a B.A. in History, Creative Writing, and Data Science (Minor) from Northwestern University.
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Ronca Realness: Voices that Sound the Sucia Body—Cloe Gentile Reyes
Echoes in Transit: Loudly Waiting at the Paso del Norte Border Region—José Manuel Flores & Dolores Inés Casillas
Experiments in Agent-based Sonic Composition—Andreas Pape
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Machinic Ballads: Alan Lomax’s Global Jukebox and the Categorization of Sound Culture
Today, SO! continues its series reconsidering the life and work of Alan Lomax in his centenary year, edited by Tanya Clement of The University of Texas at Austin. We started out with Mark Davidson‘s reflections on what it means to raise questions about the politics behind Lomax’s efforts to record and collect folk music, and continued a few weeks later with Parker Fishel‘s consideration of Lomax’s famous “Southern Journey” and how it has been appropriated by musicians more recently.
With Clement’s own article below, the series begins to rethink Lomax as a touchstone in current and continuing drives to collect, measure and compute sonic cultures, something that seems hot all of a sudden (see, for instance, coverage of recent digital analysis of trends in pop music at Queen Mary University of London). In her thoughtful, illuminating and inspiring article below, Clement challenges us to consider the politics behind these efforts to search, retrieve and analyze audio, something that the case of Lomax throws into stark relief.
— Special Editor Neil Verma
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When the Association for Cultural Equity, an organization that Alan Lomax founded in 1983, announced the release of 17,000 music tracks from Lomax’s fieldwork collections, the New York Times heralded the release as a manifestation of Lomax’s Global Jukebox project, a computational experiment for accessing and studying his vast multimedia collection of the world’s culture. The Times piece likens Lomax’s project to Pandora, which allows the listener to search for music “like” music she has already found. Lomax’s biographer, John Szwed, also makes this comparison but modifies his description by proclaiming that unlike Pandora’s recommendations which are “based on personal taste” and “tend to lead sideways . . . to production style,” Lomax’s Global Jukebox idea held the potential to point a listener to “deeper principles of cultural and musical organization” (The Man Who Recorded the World 391).
Gobsmacked by whizbang possibilities, neither the Times nor Szwed discuss the deeper principles behind Lomax’s attempt to represent culture as a global search engine. In the context of the powerful work being accomplished in the Music Information Retrieval (MIR) community and my own project (HiPSTAS) to develop software for making sound collections searchable and accessible, In this article I will argue that how we build systems for searching and retrieving and browsing cultural artifacts as data is a profoundly political act. Recognizing such politics suggests that Lomax’s Global Jukebox project serves as a cautionary tale for how social and cultural contexts — or what Donna Haraway calls our “ways of being” — are reflected in the systems we develop.
John A. Lomax Collection in UT Folklore Center Archives, Small Multiples. Instrumental sections are in red, spoken sections are in green, and sung sections are in blue. Click to see a full-size version.
The Singer with the Song
The year that Alan Lomax was born (1915), his father John Alan Lomax published a landmark piece heralding seven new types of American ballads for study. American ballads, he argues “reveal the mode of thinking, the character of life, and the point of view, of the vigorous, red-blooded, restless Americans, who could no more live life contented shut in by four walls than could Beowulf and his clan, who sailed the seas around the coasts of Norway and Sweden” (“Some Types of American Folk-Song”, 3). Unlike any other collection of ballads, John’s “American ballad” included the ballads of the miner, the lumbermen, the inland sailor, the soldier, the railroader, “the ballads of the negro; and the ballads of the cowboy . . . [and] the songs of the down-and-out classes, — the outcast girl, the dope fiend, the convict, the jail-bird, and the tramp” (3). Governed by a laudable goal to record the songs of folk cultures at the fringes of mainstream society, the senior Lomax’s view of the communities where he would collect his songs (including jails and state farms), was complex, and can fairly be called both progressive as well as racist (Porterfield 170).
John and Alan went on seven collecting trips together between 1934 and 1936 and co-authored five books on their return. On these trips, they collected songs from people on the street in cities like New Orleans and people in the country, from both church-goers and prisoners. While John held romanticized views of the “noble” southern black man, Alan, on the other hand, indicated a more nuanced understanding of the complexities inherent to his father’s attempt to generalize patterns of “folk” for study. Alan linked “the singer with the song” and was interested in the politics behind prisoners made to sing with guns at their backs and in the cultural lives of people that were so poor in means but so rich in “beautiful harmony, with enormous volume, with total affection” (Szwed 49). While Alan maintained that he was interested in the individual’s story, John believed that “a genuine ballad has no one author. It is therefore the expression of no one mind: it is the product of the folk . . . It might have been written by any one” (“Some Types of American Folk-Song”, 1).
John A. Lomax Collection in UT Folklore Center Archives, Small Multiples. Instrumental sections are in red, spoken sections are in green, and sung sections are in blue. Click to see a full-size version.
Taxonomies
The Global Jukebox project demonstrates an almost complete reversal in Alan’s concerns. The studies behind the Global Jukebox include Alan’s Cantometrics and Choreometrics, in which he produces taxonomies for studying song and dance and his Parlametrics project, an “experiment in metalinguistics,” which Alan and his collaborators describe as a taxonomy of “patterns of style” in speech based on dynamic changes in pitch, loudness, speed, spacing, rhythm, and timbre (“A stylistic analysis of speaking”). These taxonomies show that Alan’s early consideration for the individual performer gave way to a desire to make folk study more scientific as a cultural mapping like what his father espoused rather than what Szwed and others have seen as Alan’s concerns with the situated politics of individuals.
Alan’s Parlametric study serves as good example. Approaching delegates from the United Nations and soliciting mail-in samples from regions not covered by the U.N. volunteers, Alan and his team collected representative recordings of 114 languages. Then, in order to study the “generally neglected meta-communicational level” in these recordings, the team designed a rating system including 50 codes that (1) “described the distinctive features of each recording,” and (2) “tended to cluster the recordings into sets of similars” that Alan maintains anyone could “readily use” to record “salient differences in conversation style” (19). These clusters pointed to 14 factors that Alan and his team would use to categorize the cultures from which they received samples:
- Repetitiveness
- Timing
- Speech length
- Upglides
- Descending cadence
- Syllabification
- Drawl
- Empathy
- Space
- Dominance/Sharing
- Relaxed/Tense
- Noise
- Breathy
- Forceful
Using these factors, Alan makes some broad assertions. The association of clear syllabification” (the degree to which syllables run together) “is most strongly predicted among gardeners with domesticated animals” and “[t]he association of clear syllabification to feminine autonomy is suggested by the discovery that this mode of speaking predicts and is predicted by permissive rather than restrictive premarital sexual mores” (27). Further, “Dominance vs. Sharing of conversation space” is strongly correlated with settlement size and severity of sexual sanctions,” a statement that Alan immediately rationalizes by noting that “this relation between a more crowded social space, high sexual tension and increased rate of interaction seems to make good sense, even if it does not account for every possibility” (31).
These spurious and broad generalizations were what Lomax hoped to facilitate for all with his Global Jukebox as the access point for “the first numerical models of the full range of global cultural variation in holistic form” for “the scientist, the layman, and the student to explore, experience, and manipulate the broad universe of culture and creativity in a systematic fashion, with audio-visual illustrations at every turn of the road” (“The Global Jukebox,” 318). By leveraging his taxonomies of song, dance, and speech in the computer age, Alan could suddenly associate and differentiate cultures holistically and en masse.
A visualization of a song in ARLO. Click to see a full-size version.
Machinic Methods / Humanistic Questions
As someone who works in the liminal spaces between the humanities and technology, between cultural studies and critique and the machines that increasingly function both as access points and barriers to our cultural artifacts, I see Alan’s switch to generalizable taxonomies as par for the course in the digital age. My own >HiPSTAS project’s primary objective is to develop a virtual research environment in which users can better access and analyze spoken word collections of interest to humanists. We understand that in order for us to search digital sound artifacts, we have to create taxonomies, metadata, keywords and other generalizable frameworks that facilitate discovery.
At the same time that we are using machinic methods, however, we can still ask humanistic questions that open up rather than close down debates and dialogues. In a recent test for the HiPSTAS project, for example, we used machine learning to analyze the recordings in the UT Folklore Center Archives, which comprises 219 hours of field recordings collected by John and Alan Lomax, Américo Paredes, and Owen Wilson, among others (UT Folklore Center Archives, ca. 1928-1981, Dolph Briscoe Center for American History, University of Texas at Austin, Box 2.325/R). In our attempt to predict the presence of different sonic patterns including instrumental music, singing, and speech, the results of our analysis are noteworthy as the visualization shown in this brief movie demonstrates.
from Tanya Clement on Vimeo
Within the results, we see a visualization of how many seconds comprise each file (in blue) and how many of those seconds for each file our software has predicted the presence of instruments (green), speech (red), and song (purple). A subtle yet striking difference emerges in the comparison between the Lomax recordings (created 1926-1941), which are the oldest in the collection, and the others, which were created up until 1968. The Lomax recordings (primarily created by John Lomax) consistently contain the least amount of speech in comparison to what the other files contain.
Of course, there are a number of ways you can read these results. Given the conversation above, one could hypothesize that perhaps the Lomaxes were primarily interested in their participants’ songs rather than their stories. One could also think about it in terms of recording capabilities across time. When the Lomaxes were first recording, John Lomax writes, “The amplifier weighed more than one hundred pounds; the turntable case weighed another one hundred; two Edison batteries weighed seventy-five pounds each. The microphone, cable, the tools, etc., accounted for sufficient weight to make the total five hundred pounds. . . . In order to carry them in the car I tore out the back seat . . .” Even in 1967, forty years later, good recorders still weighed 70 pounds and required a car battery, but tapes were longer and costs were less. More tape and more time at less cost both financially and physically had a big impact on what researchers recorded. At the same time, the data shows that the later recordings are not much longer, but do seem to have more seconds of speech.
There is a danger in these kinds of machine-generated generalities. We employed taxonomies (instrumental, sung, speech) to teach the machine to categorize these patterns, but why these patterns? Are there others? Or did I choose these based on what I already wanted to say about the Lomaxes’ practices? And, I haven’t even mentioned here the subjective practices inherent to choosing algorithms for such work.
These kinds of questions require more research, and more contextualization than this aggregated data set can show. Just as the ballads that John and Alan Lomax once collected were written and sung by someone, so were the communities that Alan interpreted through his Parlametrics made up of individuals, not types. Perhaps Alan’s desire “to record the world” was just and Google, the collector, categorizer, and interface for all things on the Internet, isn’t evil. But the Global Jukebox Project serves as a cautionary tale about the politics behind the speed and efficiency that machinic methods seem to promise, a politics that needs to be far less opaque about its deeper principles and problems.
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Tanya Clement is an Assistant Professor in the School of Information at the University of Texas at Austin. She has a PhD in English Literature and Language and an MFA in fiction. Her primary area of research is scholarly information infrastructure. She has published widely on digital humanities and digital literacies as well as scholarly editing, modernist literature, and sound studies. Her current research projects include High Performance Sound Technologies in Access and Scholarship (HiPSTAS).
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Featured image: “Day 21 – Waveform” by Flickr user evil_mel, CC BY-NC 2.0
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Sounds of Science: The Mystique of Sonification — Margaret Anne Schedel
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