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title: "Shannon Mattern"
status: "Auto-transcribed by reduct.video with minor edits by James Parker"
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James Parker (00:00:00) - Could you begin by introducing yourself and your work just in general, before we get on to the specific questions and Machine Listening and so on. 

Shannon Mattern (00:00:10) - So I think I'm probably, uh, I would like to think a pretty, um, principle, principled, undisciplined person, intentionally undisciplined. I came from a non-academic family. My dad had a hardware store. My mom was a special education teacher, majored in chemistry and switched to English. And then, uh, when I thought I was going to go work in advertising, but instead I was encouraged in my last semester of undergrad to go into grad school, which is something I hadn't even considered really and got into a PhD program and media studies, but took a lot of classes in architectural and urban history and theory and landscape theory. And then, um, uh, became interested in information studies, uh, which not really, um, library information studies, not training to be a library and, but just kind of the more theoretical and aesthetic dimensions of that field and then did a post-doc at our history, which again, I, some not something I targeted, but was the art history department in the post-doctoral program kind of chose me, which was really exciting to be exposed to that field for a few years.  

Shannon Mattern (00:01:07) - And then, um, went to move to the new school, into a department of media studies like 16 or so years ago now, but taught a lot with designers and that's a wide variety of designers at Parsons school of design. So I have either co-taught or had students in my classes from architecture, urban design, uh, design and technology, ground communication design is still a wide variety. Um, and, uh, and my work has been inspired by all those different collaborations. My specific interest in sound as probably comes from the fact that I was, I dunno, trained sounds a bit too kind of overblown, but I took a lot of music lessons. I actually think I was a pretty good flutist. I was thinking of going to conservatory. If I hadn't gotten into a traditional kind of college, um, college pathway and then also play the piano and violin, um, uh, didn't pursue those professionally. Uh, but then I was had the good fortune of being paired with a colleague. When I started at the new school in 2004, I shared an office with a musician slash composer and just start everyday conversations really made me realize that a lot of my work about info about architecture and media was very, um, ocular centric. A lot of the architectural criticism, uh, historical scholarship really focused on the building almost as if it was objet d'art, an object of art. That was something to be seen, not something to be heard or something to be walked through or experienced. It was almost as if it was being read as a painting. And my conversations with my colleague, Barry Selman really helped me to realize all of the inaudible components that weren't being addressed in contemporary criticism in scholarship. So that really planted a seed or laid kind of a ground note. I don't know what Sonic metaphor you want to use here. And that has been a thread or a reframe that has echoed throughout a lot of my work over the past decade and a half or so.  

James Parker (00:02:58) - Great. Thank you. Uh, I mean, what an amazing eclectic, uh, uh, past and trajectory, I mean, I, I kind of, I kind of want to dive into all of that biography and everything, but maybe, maybe let's maybe let's leave that and see what comes out. Um, you know, you, you wrote this, you were saying before that you, you, you had a coronavirus and that you were, you were already thinking about Machine Listening and then, then suddenly the pandemic context gave you a way of thinking those different things together. Um, you know, could you, could you tell us a little bit about, um, you know, what, why and why thinking about the pandemic or pandemic Listening, you know, ends up being so closely concerned with Machine Listening or, or maybe also, you know, what even is Machine Listening or whatever way you want to get into that question? 

Shannon Mattern (00:03:54) - Sure. So I will say that a lot of my research comes out of prompts and invitations to contribute. So last summer I was contacted by, um, Joel, you might know some of these folks over burrata and Leslie Hewitt, two artists who teach at the Cooper union and they were organizing the interdisciplinary studies, some seminar that they invited me to be a contributor to. And they told me that I'm just looking at the email here. They had three themes. They wanted me to choose to be a part of either the expansion, the counterpoint, or the dreamwork theme. And I just couldn't decide between any of them. So I decided I'm going to write something that was on all three. And because I had in the past thought a lot about Listening to infrastructure about Listening as a methodology, to help us understand logistical systems and infrastructures. I thought, um, I wanted to find an application that would allow me to weave together those three themes for the lecture series. So this is really productive for me to have either, um, A, a framework of a term to bounce off of a lot of my work. Doesn't just erupt from my brain. It usually comes from somebody putting a constraint out there. And then I figure out like where my interests in, what I can do well would bounce up against this, um, kind of prompt that I've been given. So that's where the article came from. I kind of hatched the idea of last summer before coronavirus or anything was kind of a glimmer in anybody's. I finished the piece, I shared a dance with it at the Cooper union in early December. I think it was. And then, um, I write for places journal, which is an open access venue. A lot of, most of my writing has takes part through places and it's a couple months review process, uh, really intensive editing with, um, my, the team there. And by the time it was being prepared for publication and early spring, the pandemic was that upon us. And I realized that a lot of the ideas that I was thinking about back in December had new resonance and this new context. So I took a lot of what I already had produced or put together in December and add it, wove some more pandemic or kind of a quarantine related themes throughout that existing piece. 

James Parker (00:06:02) - So, so if, if the pandemic didn't supply the immediate context or the media inspiration for the essay, but then was it Machine Listening and you know, what do you understand? Machine Listening to be, I mean, one of the things that interests us is how to name the problem that we're concerned with. And, um, you know, in the, in the research that we've done, it seems that Machine Listening has a couple of different sort of origins, you know, um, comes out of a scientific discourse where it's one of the terms used by scientists to talk about the application of AI and machine learning techniques to audio, but it's not the only one. And then it seems that there's a, there's a sort of a parallel or related stream into thinking about Machine Listening via, um, computer music. And, um, there's a number of composers who work with computers who talk about Machine Listening. And so we've been trying to think about, you know, what, what is this object? And I'm just, I was just so struck by your essay that it names this object Machine Listening, and that it seems to have so many concerns, similar concerns to the ones that we do. And I was just wondering, you know, how, how do you think about Machine Listening how does it come to you as a problem? Why, why should we be concerned about Machine Listening um, in, in the context of the pandemic or otherwise, 

Shannon Mattern (00:07:21) - I think maybe the origins of this piece were very similar to what I mentioned earlier, um, uh, in terms of ma the birth of my interest in sound in space, because I was realizing that just by talking to a musician colleague that this was something that was not echoing and a lot of the scholarship and criticism I was reading, uh, all throughout 2019 and the prior years I was hearing so much about Machine vision. Um, so, uh, concerns about automation, about facial recognition, about the inherent biases and injustices that are kind of rooted into programmed into this technology. Um, uh, I thought there's parallel stuff happening in the Sonic world that isn't really being as dressed as much. So just hearing the, um, absence, the silence on relative silence on this compared to the proliferation of, of research and criticism, and I think kind of general public growing public understanding of the political problems of machine vision, I kind of wanted to contribute to help people to realize that other sensory modalities are being automated, um, as well. And we need to maybe transport or translate some of those critiques apply to machine vision over to, to see how they work in the Machine Listening realm as well. 

Sean Dockray (00:08:34) - Why do you think that, why do you think there was a lag between your sort of computer vision and Machine Listening as a object of interest? 

Shannon Mattern (00:08:43) - Uh, well, I think that it's part of just this general critique, the fact that, you know, a lot of the founding figures in sound studies when they had to justify the existence or the need for such a field were critiquing this long kind of Western enlightenment tradition, that's privileges of ocular centrism. The fact that we kind of have this epistemological bias towards sight as the quintessence or the, uh, paradigmatic or quintessential mode of knowing. So I think that's one kind of larger macro skill framing for why not much attention is relatively as paid. There's also the fact that we don't have necessarily a cultural literacy or even like the linguistic elasticy you talk about sound is the way that we use to talk about vision. Um,And I, I think it probably is not as well known, given how much press has been offered to especially facial recognition and the fact that many cities have actually officially outlawed it. This is in major newspapers, um, and the, the lay public, or kind of general readership knows to some degree that the machine vision and all of its various manifestations exist. There's much less press, uh, given to kind of the Sonic components also. So why these larger historical epistemological disciplinary framings, and then I think kind of the general news agenda is there's a dearth of attention to these. So those are just two factors why there might be less in this story. 

Sean Dockray (00:10:06) - Um, you mentioned that your previous research in, in, um, infrastructural Listening or Listening infrastructure, um, as like, uh, as one of the earlier threads that brought you into right. This most recent piece, I was just wondering if you maybe could walk through, um, well, what, what you mean by that, by, by listening to infrastructure and then how that connects to how that brought you to, um, 

Shannon Mattern (00:10:29) - Sure. So I think I started by, again, those early conversations with my colleagues 15 plus years ago, and I was writing a lot about libraries. So my dissertation, which I did in 2001, I think is what I did the field work for that was at the Seattle public library. So I wanted to, I was essentially an ethnography of the coming into being of a building. So I looked at how the plan's evolved, how it responded to how you had this kind of mixing of cultures, this you upper European avant-garde architect coming to Seattle that was establishing itself on the world map, thanks to the presence of Microsoft, et cetera, and how a new kind of stylistic vocabulary came into being through those negotiations. And I didn't talk much about sound in my dissertation. And then when I turned it into a book, I realized like this was a real gap in my initial field work. 

Shannon Mattern (00:11:17) - And I realized that a library is a building typology that is defined in large part by its specific sonnet conditions. You know, the historical myth is like the shushing environment, which is not really so much the case anymore with more noise making activities happening there. So I wrote a couple articles and incorporated quite a few, uh, library and archive related themes looking at either Sonic collections, Sonic archival collections, or, um, uh, ways of listening to archival materials or the archival residences and acoustic conditions of information architectures themselves. So that's one way. And then I started to get a lot more interested in infrastructures when that word became a lot more popular in the Academy, especially in media studies, which is the field that I guess I most identify with and, um, was thinking about what we can learn there, there was so much, um, talk of making the invisible, visible. 

Shannon Mattern (00:12:10) - That was honestly annoying me. It was so prevalent. And I wanted to know again what we could learn by about infrastructure by listening to it. So I wrote a couple pieces about artistic work methodologies, either work in like structural engineering, where people are putting contact microphones on bridges and dams, et cetera, to essentially hear structural weaknesses that are not visible, um, or are not detectable in other ways. And then more recently I wrote a piece about, um, uh, Listening to logistics. So a lot of that work in media studies on infrastructure has now expanded or morphed into an interest in supply chains and logistics. So I wrote a piece, a chapter a couple of years ago that still isn't out yet. I think it's probably gonna come out in the book of 2021 because academic publishing moves painfully slowly, but that was about what we can learn about supply chains by Listening both up close to machinery docks, historical kind of vocalizations of stevedores, et cetera, and at the macro scale. So it would distant Listening B to an entire kind of global supply chain, for instance. So that's like a larger scale manifest. So I guess I started at the architectural scale by Listening to a reading room or listening to an archival collection, then scaled it up to infrastructure. And then even further to think about what we can learn by listening to supply chains and logistical systems. And then this urban oscultation piece was the most recent piece. And that I think kind of crosses crosses some of those scales 

Joel Stern (00:13:34) - Shannon. Um, I wonder if you could say something about, um, the kind of distinction between, um, human and an auditor's in relation to infrastructural Listening and whether you see that as sort of a hard distinction, but you know, between human Listening and Machine Listening or whether it's always sort of somehow blended and, and interdependent human and non-human, um, auditors' um, as you put it in the essay, um, in relation to infrastructure or Listening, um, and you, you kind of, um, talk about how those sort of, um, two forms of Listening often interdependent or, or blended. Um, and, and I suppose when we were, um, thinking about how to define Machine Listening, you know, as often Listening in the absence of a heap of a human auditor, or it has to be fined again against the human, um, somehow, so I'm just interested in, in thinking with that binary.

Shannon Mattern (00:14:35) - Sure. So this is kind of not very funny, but it's a joke with my editors and everything always comes back to a piston ecology for me. So how, kind of the ways we know the world are manifested in the way we design a materially, everything from the scale of an object to a piece of furniture, to an architecture and an infrastructure. So for me, like the biggest and most interesting distinction between human and non-human and Listening is the different epistemologies ways of knowing modes of sensation, how they're operationalized in the world, which we'd like to think of as kind of being pretty distinct. This is part of the critique of the, of hyper automation that we think that there so much nuance and poetry that is lost when you rely on Machine vision or Machine Listening, but there's actually a lot of kind of coded regimented stuff that happens with the way human ears listen as well.  

Shannon Mattern (00:15:22) - So I think maybe looking at the different ontologies methodologies that are inherent in those, um, and then the cultural specificities as well. I mean, this is something that anthropologists and archeologists have to offer and realizing that there's not one naturalized motive Listening or not one epistemology or ontology that is constructed through the practice of Listening. So, um, just seeing all the differences that can be illuminated and Harry I'm illuminated, what's the Sonic equivalent of that kind of rendered audible through kind of those disciplines and how we might find them parallel with the way we kind of build algorithms and machines to listen for us. Um, so it, it, it draws attention to, and helps us to better understand like both. And again, it's not a binary, but the, if we want to just simplify for the sake of this conversation, it can shed light to here. 

Shannon Mattern (00:16:11) - I keep going on with the ocular centrism, I'm just going to do it. It illuminates what it means to listen as a human and what it means to listen to the Machine when you can kind of have them have a counterpoint, be counterpoints to one another. And we also recognize that they can be in a productive relationship. It's not a matter that all automation is necessarily bad. This is, again, something that I think we can learn from the critical discourse about machine vision and that rather than the, just entirely doing a way with algorithmic governance or with a facial recognition, there are ways that, for example, like heat mapping or some type of machine vision can be useful, helpful for public health officials or, um, ecologists, for instance, to be able to identify areas that then you can, um, pinpoint more kind of on the ground thick data kind of methodologies. 

Shannon Mattern (00:17:01) - I think a similar thing could be applied in Machine Listening and that Listening doing that distant Listening, Listening kind of at scales beyond the capacity of human ears, even collective human ears can help us then to determine how we can better deploy or more effectively employ humanistic methodologies to better understand particular phenomenon. So that's just one example of ways where we can think about the different affordances of human and non-human ears to be used in tandem, rather than looking at the machinic as an, a ratio of, or a threat to the more poetic, humanistic, um, ways of going about things. 

James Parker (00:17:41) - Could you maybe give a couple of, uh, concrete examples of, um, Machine Listening that we sort of, that should be valorized and, um, you know, I can think of a couple from the essay, but, um, I don't know if there's anything that you particularly interested in discussing or you find particularly powerful. 

Shannon Mattern (00:18:00) - Um, well, a couple that I didn't mention in the essay were kind of ecological applications where you're Listening at macro scale for, um, acoustic ecology. For instance, you could see how, for instance development in a particular area kind of real estate development or construction is perhaps driving out particular species. So the other remaining species are kind of shifting the pitches of their bird calls or the kind of symphony of animal non-human voices is changing in relation to human activity on the periphery, that kind of larger things in a Machine Listening 24 hours a day over the course of months or years that a human couldn't sit on a corner and do, for instance, could then offer an interesting kind of sampling. It could provide an interesting or helpful mode of sampling or, uh, the capacity to choose a really particularly rich field site for a human researcher or a team of research. 

Shannon Mattern (00:18:55) - Then go in another area that I was just reading about tonight, there was an article on efflux about kind of aquatic Listening and underwater soundscapes. So particularly thinking about how a lot of the underwater extractive work that's happening and Naval research for instance is changing, um, acoustic ecologies aquatic, acoustic ecologies. That could be a way where kind of, uh, specifically strategically deployed Machine Listening sensors are then helping humans to better target their activity. So those are just a couple examples. And then also I mentioned the structural engineering, so monitoring the security of things like dams and bridges and other types of really important kind of physical logistical architectures.

Shannon Mattern (00:19:38) - Um, you probably, for the sake of the maximizing public security, you want to have multiple ways of, of, uh, monitoring the structural soundness of these things. So both having cameras trained on them having kind of periodic inspections by trained inspectors and potentially having round the clock Listening to making sure the machines are working as they should, that the bridge is vibrating in a consistent way. And then if it's not, then, you know, to deploy a team to take a closer look or listen to it, 

James Parker (00:20:10) - Those are great examples, quite, quite, I don't want to say not optimistic. Um, but listening to you, you talk about Machine Listening that way I'm just struck by, by the fact that the predominant forms of Machine Listening at the moment are not, and I always worry a little bit, you know, in my own thinking it's often been, you know, disability, discourses and things, you know, it's so obvious the, you know, the role that a voice assistant for example, might be able to play, uh, Machine transcription and so on, you know, it's not possible. You can't, you can't have a politics and Machine Listening, that is kind of, you know, Chuck it all out. But at the same time, I feel that like those kinds of, um, applications do a certain amount of work politically to smooth the, um, the entry of Machine Listening forms of Machine Listening that are so overtly tied to capital that the whole methodology is kind of bound so deeply up with, you know, platform capitalism or surveillance capitalism or whatever you might like to call it that I always feel, I always feel a bit nervous actually about, about foregrounding, the benevolent applications of Machine Listening because, um, because I just feel like that the, the juggernaut it, you know, is coming, uh, and the, the, the scale of the challenge is so significant that I, yeah, maybe it may be, I'm just commentating on my own psychological processes now, but like, you know, I, I worry, I worry about, I'm just, I'm just really worried about the beast that's coming. And I, I kind of, I'd be interested to hear your reflections on, you know, the politics of Machine Listening more generally, and some of those, you know, you list many nefarious applications, uh, in your essay and how to think about those together, um, with the, you know, the more benign ones.  

Shannon Mattern (00:22:14) - So, absolutely. I mean, part of it maybe is the fact that I'm so immersed in all of the, um, uh, critical and in some cases, alarmist literature. And actually, I don't even know that it's fair to call it alarmist because these things are already here, they're already happening, and we need to be aware of them and the kind of the really frightening and nefarious applications that sometimes I want to think about rather than throwing the baby with the bath water, recognizing there are potentially kind of, pro-social not benevolence because there's always kind of an under a potentially exploitable undercurrent there, but, um, more positive for lack of a better term applications, but you're right. There are myriad, um, uh, exploitative extractive, pick your negative adjective kind of applications of these types of technologies to, for surveillance purposes, for policing, for, um, given your work on borders and migration, thinking about how to determine the fit of particular asylum seekers, for instance, to assess the veracity of a claim for migration or asylum seeking, which is something that I know that several folks are writing about and that Lawrence Abu Hamdan's artwork is kind of is, uh, is addressing. 

Shannon Mattern (00:23:30) - So there are lots of contexts aggression detection, um, uh, gunfire detection, uh, we could go on, but this is to the ones that are coming immediately to mind right now. But yes, there are myriad cases that are not just even speculative applications that are actually in practice right now today and are doing harm, even things that feel a benevolent or kind of innocuous like, um, voice assistance. This is something I think some of the other folks you're going to be including in your curriculum, like shockingly you're talking about is the, the default, the training set for a voice assistant pro, um, and it involves so many kinds of acoustic assumptions, which naturalizes or normalizes certain voices and renders anyone with an accent. For instance, often kind of a model called communities of color rendered inaudible to the Machine, uh, which makes, uh, perfectly functional bodies feel abberant, um, and their whole politics of disability, um, that are woven into a lot of assistive technologies as well. So these, again are things that are framed as being benevolent applications of the technology that have perhaps in some cases, unintended, or maybe Trojan horse, uh, kind of type of, um, more nefarious dimensions or applications, uh, on the flip side,  

Sean Dockray (00:24:51) - It's quite a, quite a knuckle knuckleball of a question. I think also it was a trap because I was like, can you please tell video the good things about procedures? And now let me tell you why it's actually bad, those good things actually dictate society for, you know, um, it also made me think of like, when did you know which she wrote in, um, control and freedom, which is in a certain way that these alarmist narratives also kind of normalize existence of these things, the presence of these things in society. So like, like even we might feel good about being quite critical and alarmist and everything, but at the same time, it like that almost that almost makes us that almost acclimates us to the presence of these things around us, almost as much as the kind of utopian narratives. And she kind of says that we should be looking at the actual, uh, the actual functioning of how these things operate in society, which I do think that your work Shannon is. 

Sean Dockray (00:25:55) - Um, although, although you do, you are quite aware of the ideological kind of role of a lot of these things. I think that, you know, that it's something, your work is often deeply materialists and it's looking at how, how these things are actually implemented and how they actually work in the world. Um, that this isn't a question, I guess I was just sort of following on, uh, just thinking about that exchange and that kind of like that tension between, um, between the alarmist and the utopian, you know, like, and then we get into this like stalemate, this kind of like loggerheads and  ...  offers. That is like one way out is to look at the actual function. 

Shannon Mattern (00:26:39) - I'm glad you think I do that too. I mean, I don't know that it's even something I intentionally started projects saying that like, I must have an explainer part of this article, but I think that might be my intention or that big, my nature and that, because I'm interested in ways that ideals are operationalized, which means the methodologies they're kind of the theoretical methodologies and then how they're actually deployed with available technologies. Um, and, um, and then again, what epistemologies they embody? I think just the fact that those are questions that are always in the back of my head for everything I'm working on, that it does lead me to want to understand how concretely something is operating. And I think this is part of, kind of the whole, this is not a new concept, but the whole movement towards infrastructure literacy that Lisa Parks and others talk about, um, in order to understand kind of like how satellites are embodying a particular geopolitics or how our cell phones work, all these things that are naturalized. And invisibilized because of their ubiquitous seamless presence. We really need to understand the physics in many cases in the mechanics and the electrical engineering by, and the, and the regulations and economic policies through which they operate, because there are value systems and etiologies and modes of governmentality that impact our everyday lives and kind of politics and, um, any, um, uh, in, in, um, kind of reinforcing and modes of inequality too. They're built into all of those seemingly bureaucratic technical things. There's much more at stake there. And just the wonkiness. 

Joel Stern (00:28:12) - Um, we, we were chatting with, um, of Vladan Joler, uh, a few days ago about his, um, work with Kate Crawford that the, um, anatomy of AI and, and one of the things he, he was saying is that, um, one of the difficulties of, um, sort of producing a material critique of, of a voice assistant is that, uh, saved intangibility of, of the voice, um, and of sound and Listening that it's sort of hard to represent structurally in material terms. Um, how have you sort of found, um, that side of things and, you know, what, what are the, um, difficulties of writing a kind of material critique of sound and Listening  

Shannon Mattern (00:28:56) - Sure. So this is what some of the earliest scholarship and sound studies, I don't know, again, there are, people are writing about sound for a long time, but I'd say like early two thousands when the field was kind of burgeoning. So for example, like Emily Thompson's book, the soundscape maternity, she is essentially rewriting urban and architectural history saying, how can we rewrite this history, uh, without necessarily having access to vast archives full of record of urban recordings? So it's a way of finding ways of Listening at media of arc of records in other modalities. So how could you listen to a photograph? This is something like Tina camp has done in her book, listening to images too, which is really kind of a really valuable contribution to kind of critical race studies as well. So what are the different politics that can be revealed by trying to discern or extract extract is such a, uh, a rapacious verb, but it's trying to kind of pull out or productively, um, uh, uh, interpret.

Shannon Mattern (00:29:56) - Sound from a tactical or a visual medium, for instance. So I drew a lot of inspiration from Jonathan Stern's work. Emily Thompson's work teeny Campton more recent years. And then for my book, which came out in 2007, I had two chapters about sound. One is about kind of the, uh, city of the wired city, the city of telecommunications. And the other one is about the city of the voice. So how kind of, uh, the oral culture, um, and the acoustic demands for it, informed architecture and urban planning for thousands of years. So there, again, we have some writing about it, kind of the Truvia and other kinds of architectural historians, um, even before they were called that we're writing about acoustics and we have kind of material traces of these cities that can give us some clues as well, but we don't have audio recordings and make cases to rely on. 

Shannon Mattern (00:30:45) - So how can we use in some cases, speculative methodologies, how can we read literature or look at artworks or photographs to, um, hear what is represented visually in those records? So this is a matter kind of an interesting mode of triangulation that requires triangulating methods. And in some cases, speculative methods, there are folks in archeology who practiced kind of Archeo acoustics, where they, um, think about how a space was made, what Machine, what it's, um, material composition was the dimensions of that space and what type of Sonic activities might have happened. There were from which you can then draw kind of inferences about modes of public engagement, the type of governments, the type of, kind of religious activities that were happening. So again, some were hardcore positivists archeologists are critical of this mode because it does require some poetic license and speculation, but I think there's something kind of really beautiful about this triangulation of methodologies. 

Shannon Mattern (00:31:45) - So these are some of the challenges methodologically in addressing sound, something that seems like an ephemeral and immaterial medium. Um, but also I think the potential for this is something that Sean's were kind of addresses as well of alternative modalities republication. So again, over the past 20, 30 years or so, maybe even longer, um, scholars, artists, this is where again, what artistic work becomes very useful thinking about sound artists work, um, the use of interactive publications that allow you to actually incorporate virtual reality or sound recordings, um, can, uh, create different modes of an effective argumentation that a traditional textual publication can't. So these are some other ways of making an argument about sound, um, that, um, newer technologies make possible that warrant perhaps kind of in, in centuries or decades past. 

Sean Dockray (00:32:38) - I'm wondering if like, with, um, with that answer, that was really interesting to, to imagine, to see how scholarship can, can sort of study the sounds of sometimes we have no recordings of, and I, and, um, so to me that, or for me that opened the door to thinking about also the silence, like what would the silences be of, of some, um, some of these other times and places and dislike, um, that could be approached in a similar way, and also just, uh, thinking about historicizing silence as a, as a project and how that shifts over time as to what silence means. Exactly. And I was thinking about that at the beginning of your essay, you talk about the new sounds and silences of the pandemic. And I think in that case, the sentences are referring specifically to the fact that there's a lot less activity on the street and, you know, it's just quieter. 

Sean Dockray (00:33:29) - Um, but I think, yeah, I was wondering, um, if you could expand on that a little bit, because I, I feel like the new silences have a regime of Machine Listening like, do mean a different thing than the silences of, for example, you know, this burgeoning industrial city, that's like, you know, where there's tons of construction happening and they have to invent the decibel to like the discussion of loudness that you have, which seems very particular to a particular urban context that quiet and silence might mean one thing, but I wonder what Clayton silence might mean now. And if ultimately, like, I wonder if, you know, silence even makes sense to a Machine or what if it does then what silence means to, um, a Machine listener. 

Shannon Mattern (00:34:13) - Uh, that's a great question. Um, I'm thinking about, and I forget the name of the researcher who has been, um, for a large portion of his life who has been tracking kind of like the a hundred square feet of silence or something. He's looking at the fact that, you know, as we are cha um, global supply chains, air travel is essentially, um, infiltrating even natural soundscapes. So there's really no silent, um, corner of the world anymore, which presumes that the lack of human Sonic interference, that condition equals silence. When it doesn't, there's a rich kind of potentially loud ecology there that a machine or a human era, if it were present would hear there's just not the Machine excels. There's not the sound of kind of industrial infiltration. So the fact that noise itself, and there's been a lot of theorization about noise as being this highly kind of culturally specific subjective.

Shannon Mattern (00:35:04) - Definition that is rooted in race and class and gender to some degree. I think what constitutes silence to us is similarly kind of culturally determined. I'm just following that thread thread a little bit further to go back to what I was saying earlier about methodologies and the available historical records and resources. We have to piece together historical soundscapes, or even contemporary soundscapes. There's been a lot of research and kind of archival studies and history about our Carville sciences silences or, um, or, uh, gaps or absences. So what voices, and this could be both literal voices or metaphorical voices in terms of subjectivities are not represented in the archive. Um, on these are cases where sometimes you, you use speculative methods. So DIA Hartman proposes the method of like critical fabulation where you use some kind of narrative visitation or speculation to imagine based on what the gaps are, what the contours of the boundary of that gap would be. 

Shannon Mattern (00:36:01) - For example, slave narratives, for instance, voices that are so marginalized that they were not regarded as, as worthy of or necessary to be preserved for posterity. So how are some of those silences then kind of either kept open to Mark those historical absences, or how might they be filled in through kind of speculative or triangulating methods? So that's going again, more along the cultural methodological thread. And then in terms of the way I was talking about it in the article, I was thinking more of just the, um, uh, I don't want to say the word objective, but the decibel level of the city has gone down dramatically. And this is again, we're kind of, naturalizing normalizing the decibel as this unproblematic way of measuring some kind of quantitative or quantity of sound. But these are the kind of the, some of the silences I was talking about. 

Shannon Mattern (00:36:50) - In fact, there were many fewer cars in the city that people weren't going to work that cities were noticeably quieter. There were some articles about how seismic researchers were able to do some things over the past few months that they haven't been able to in a long time, because the world just quieted down to some degree. But I don't know that anything, if we can say that there's ever kind of a zero point of silence, because even before human ears or, um, mechanical ears were present to listen for certain things, there were other, I've kind of talked myself in a circle here. I'm not where I'm going, but, um, I don't know that there is such a thing I would actually, I'd be curious to hear what you have to think about your own, but how you would respond to your own question. But I don't know that aside for outside of an echo outside of a kind of acoustic, was it called not, we say that again, 

James Parker (00:37:42) - Anechoic Chamber 

Shannon Mattern (00:37:43) - It outside of that, I'm not sure that it's possible or outer space. Um, if in a realm of, or kind of organic life, if silence is a possible condition, I don't know. 

James Parker (00:37:57) - We're in a, we're fast getting to a tree falling in the woods territory.  

Sean Dockray (00:38:03) - We there's one, one very particular Place we can look at in the, in the article, like for me. And that be the, um, well, in the, in the sense of like silence as like that, which doesn't exist in the archive. If we take that as like one definition of silence, then the kind of like total total kind of like distributed Listening and accumulation of data presents like almost no gaps, you know, if we take it to its frameless, this kind of conclusion, um, but you kind of mentioned, um, at one point that there might be the opportunity to choose not to listen to certain things, right. That somehow in developing these psych, um, systems and Machine Listening, we should, we should, as a S as a society or whatever, determines that there are certain things we want not, we want to not listen to that should be inscrutable, I think is the word that you use. So I was thinking about that inscrutability as a form of silence, but that silence is not a natural thing. It's something it's like a political thing that we have to strive for. So I guess I was thinking like, maybe that would be one, one Mo mode of thinking about silence in this condition. Yeah, 

Shannon Mattern (00:39:13) - I agree. And this is again, drawing some inspiration from work that's happening in the visual. Machine looking Machine, um, vision realm where, um, some cities are deciding just not to record, not to put up cameras or to, um, uh, produce, um, artistic modes of resistance, cultural used to call culture jamming, um, or to, um, uh, practice, um, uh, to engage practices of refusal. I mean, there are different kinds of political communities that use these different words of resistance refusal. Um, uh, non-use all of them have different political balances, but these are ways to kind of produce silences. Maybe you could say that they're a principal choice to just not participate to either not render your face visible to Machine, to not render your voice or other kind of Sonic emissions.

Shannon Mattern (00:40:06) - Audible to a Machine or to just not have the Machine altogether. And there might be some other kind of alternatives within, um, kind of within that span, but these are ways that we're kind of we're producing Sonic absences or silences in the record. I guess we could say 

Joel Stern (00:40:23) - One of the things that, um, Lawrence Abu Hamden, who has done a lot of work, um, thinking about the politics of silence and, and silencing, um, said in a recent lecture unsound blade, was that, um, with Machine Listening and sort of forms of forensic Listening, um, that we have now, the distinction between background and foreground has really sort of been dissolved to a degree that what we used to think of as background noise is now justice easily assimilated and sort of analyzable and, and, um, operational. Um, so it perhaps, you know, our understanding of noise and silence is also tied up in these notions of foreground and background, which have a lot to do with, with human audition. Um, and they start to, um, become sort of much blurrier with non-human audition. 

James Parker (00:41:18) - Could I just actually follow up on that, that, because I was going to say, if we think about inscrutability and foreground background, you know, a pattern I was looking at recently that, um, is for a form of Sonic branding, um, they give the example of, um, uh, soft drink cans where the, um, the, the pattern was for a meth.  

James Parker (00:41:42) - Basically you would, you would design the ring pull so that it would make a sound that was particular to that brand of soft drink. But that, that, that it wasn't, that wasn't, that wasn't, that sound was not designed for human hearing, so that there's no necessity that a person be able to distinguish between a Coke and a Pepsi or, you know, whatever, but that the sound would be legible interpretable and comprehensible to the machines that were Listening in the home. So, you know, then, and so that immediately makes me think of, um, you know, this idea of operational uh Listening you know, which you kind of comes out of Trevor Paglen, thinking about the operative image for archi. And, and that sounds that are not in tech no longer need to be intended for human is right. That where's the, where's the foreground and where's the background in the, in the pulling of that wrinkle. Right. Um, that, that, that flipped, um, for, for me, it's the sound of forthcoming refreshment and for the Machine it's crucial brand data that, that, you know, it can be linked to whatever it is that that terrifying regime is linking it to. Yeah. So just, yeah, just in terms of thinking about, you know, science inscrutability foreground background, it seems like that's relevant.  

Shannon Mattern (00:43:09) - Absolutely. I mean, this reminds me of another piece I wrote a couple of years ago about it's called things. That beep it was about the history of Sonic branding. It was just a short piece, but I also wrote it in kind of an online venue so that I could include audio files and video and kind of video of historical advertisements. So I'm looking at things like the Sonic branding of espresso machines and vacuum cleaners and car doors, which Kristen bistro, um, kind of, um, what's her name? Karen Bijsterveld has written about, um, all the wood chip bags and then up to kind of the beeping and the Sonic branding of our machines and how certain aura or persona are attached to particular platforms based on the beeps and chimes that they make. So, but yes, thinking beyond that, to how this type of branding still applies, if you're kind of producing sounds for Listening machines, but, um, yeah, that's a really interesting direction to take, to sort of extend that research.  

Joel Stern (00:44:05) - I mean, I actually, I'm just sort of tempted to, um, bring us back to, to sort of a slightly earlier point in the conversation. And, um, we've been talking, um, James, Sean, and I about the need sometimes to ask sort of dumb questions to get, um, you know, the, um, in a way, a broader sort of answer. And I guess I was just thinking about, you know, the pan, the pan acoustic on, and this sort of, um, experience of, of living in a society, um, in which you feel that you're constantly being overheard, um, and your Sonic worlds are sort of captured.

Joel Stern (00:44:44) - Um, and, um, scrutinized and instrumentalized. And, uh, I just wonder if you could say something about how you understand the, kind of the social experience of living in a, in a panic, acoustic sort of context, and, you know, in terms of human relations and behavior, and, and I guess it comes back to, you know, we were sort of talking about, um, certain dystopian and utopian horizons of Machine Listening and, um, if we sort of think about, um, the pan acoustic context in social terms, and in terms of its social impact, do you have a, a sort of, a way of summarizing your, your kind of sense of, and, and feeling, um, as to what, what that, what that entails? 

Shannon Mattern (00:45:29) - I wish I had thought about that more, but, uh, I just thinking about it specifically, in terms of the pandemic, remember there was some discussion on, I actually did see shortly before I published this piece. I wanted to see if anybody had yet patented kind of a pan acoustic technology that will allow for kind of diagnostics of Listening for a particular kind of cough or any types of Sonic indicators of illness that could be used for COVID-19. And sure enough, some people would put up on archive X, some examples of kind of systemic diagnostic Machine Listening on of technologies. So this is something where I can imagine a lot of kind of stifled sounds, um, uh, use of technologies to kind of, um, garble or mask the voice. Um, just as we, as we see the rise of both techniques and technologies to thwart recognition through visual technologies, I would imagine similar things would be happening, or we could have more strategic silences people just not talking in public spaces where they're pretend where there is the potential for eavesdropping. 

Shannon Mattern (00:46:37) - So again, there's that potential for using society using silence and refusal strategically, or using kind of these techniques and technologies for masking. So those are a couple examples, and those could be at multiple scales of design, they could be gadgets, you know, face masks that have kind of Listening and speech altering capacities to the long history of using kind of architectural acoustics to do these types of things, to do the incorporation of kind of, um, ambient technologies within an architectural space or an urban spaces. So there could be multiple scales of deployments of these types of potential masking technologies. Again, I got a hesitant haven't thought so much about that myself again, I'd be curious to hear if any of you have any thoughts about these potential short or longterm social applications for the Penn acoustic con, 

James Parker (00:47:26) - Could I, could I just ask as a follow-up, um, where you think a sort of more systemic critique rather than a kind of responsive, reactive, um, masking, um, sort of response to audio surveillance might, might begin, or what do you, you know, you said before that you've read A lot of stuff from, you know, Machine vision and so on, and some of that's beginning to get traction. I mean, I guess one of the things we are interested in is what, how, how could we, you know, help to produce a systemic critique of Machine Listening? And I wonder if you have any reflections on how strategically you might go about that? 

Shannon Mattern (00:48:05) - Sure. Well, there is the potential again of regulation refusal. This is what some people are when they're, when they're calling for a systemic approach to, or resistance to Machine vision or surveillance, rather than just asking individual people to produce kind of individualized consumer responses there instead in calling for regulating, breaking up big tech, et cetera, holding big tech accountable for thinking about the potentially nefarious applications of their technology. But then there's also the larger root issues that some of these things are getting at like the policing applications, the fact that we're using machine vision for asylum, for instance, these are larger things that go way beyond the use of the voice as a diagnostic. We have to think again about like larger patterns of like climate change in, in forced migration patterns or the breakdown of criminal justice and social justice infrastructures in our society. These go way beyond this band-aid application and Machine vision, and Machine Listening as, um, stop gap measures to fix broken systems. So these would be kind of going even beyond just critiquing the system or fixing the system of Machine Listening itself to fixing the larger entangled systems to which Machine Listening is an insufficient and partial proposed solution. 

James Parker (00:49:26) - I mean, it sounds like that's a critique of sort of techno solution is, um, you know, in the context of the pandemic that's, you know, just being so clear the techno sort of the drive towards techno solutionist responses, you know, in relation to contact tracing apps. Um, you know, you mentioned before COVID diagnostics, we've been, um, Looking at a number of the, these organizations. One, for example, could VOCA AI put out in March a call for people to provide voice samples, um, for the purposes of producing a diagnostic tool and they had 30,000, um, they had 30,000 samples generated within a couple of days, right? So it's not just that the company, you know, the companies, uh, sort of, uh, pushing, um, techno solutionist responses, but there's a sort of broad cultural buy-in to people wanted to do that bit, right. So they provided their samples and, you know, the, the legal terms of that provision were extremely murky. And this is a company that provides voice assistance to call centers. Um, most of the time, you know, and so it's not at all clear what the possible impact, I mean, not trying to sort of impugn them it just to say that it wasn't clear at all. 

James Parker (00:50:39) - And, but there's a desire to contribute to these kinds of technical and political responses to an extremely unsettling situation. I mean, and there's the question of the role of the way in which big tech is moving into the health space? Um, more generally in relation to the pandemic, I was really interested actually, the way that sort of, it seems like, you know, you, in the piece, you talk about the way in which the city is often metaphorized as the body. And so if we think about Listening to the city, then, um, you know, you talk about the stethoscope and then immediately it becomes a kind of a diagnostic, um, relationship and just Listening as a form of diagnosis. Um, it just seems like there's like, uh, there's something interesting going on Listening as diagnosis and the way that specifically unfolding in the pandemic context and the way that machines are increasingly work, moving towards diagnostic or kind of forensic applications. 

Shannon Mattern (00:51:36) - And the diagnostic that is used is, um, the first step towards predict kind of predictive applications, right? So, um, and then there are, again, plenty of nefarious applications there, but just this idea that people feel compelled, or even, um, as if it's a civic duty of, to contribute a voice sample to these, these applications. I mean, these are, I think a couple different phenomenon that are converging here. One of them is the fact that the use of technology there've been several people who've written about this, the use of technologies and crisis situations tends to normalize them. And it allows for their convenient application that go well beyond their initial use for a crisis. So this is the case where you have convenient mission creep, or you have something that is actually institutionalized for the long-term that was originally deployed under an ostensibly kind of delimited application for a specific crisis context. 

Shannon Mattern (00:52:29) - And then you also have kind of the magical thinking that all the allure of isn't it amazing what machines can do. I mean, I remember even you had mentioned that, um, Trevor Paglen, his work, and you also mentioned flattens work with Kate Crawford. So the project they did together at the, was it the Prada foundation where they were using? I think it was, I forget what image now. I think it was imaged debt. They were using kind of image net, um, a data set to look at facial recognition and lots of people were contributing, I think, at the exhibition and online contributing their faces to say like, isn't it interesting or funny to see how wrong the Machine or how right. The Machine is about me, but there was also a discussion among a lot of African-American folks are sorry, are black, not only African-American, but black community on Twitter that I don't want to contribute my face to this. 

Shannon Mattern (00:53:17) - It might be a fun kind of diversion or a, um, a novelty to folks who aren't already typologized and surveilled to see how Machine sees you. But if you're a part of a marginalized community that is already, already, always already typecast by this type of technology, I don't want to voluntarily contribute my face to the data set too. So this is, these are some of these compulsions that there's the novelty and entertainment value of contributing just to see how the Machine looks and how right or wrong it is. Plus also the crisis context that gives, gives people a sense of this is almost like a civic duty to, to give up your data.  

Joel Stern (00:53:57) - No, that's exactly right. And I think, um, you know, why 30, that's why 30,000 people would offer up their coughing samples to like an AI and in a couple of days. And also because the allure of, um, being able to get a diagnosis instantly over the phone, rather than having to risk, you know, going to a hospital or a doctor is such a powerful one. So they kind of imagined payoff is sort of, you know, very seductive. Um, and it's very tempting not to.Sort of think in, um, a broader infrastructural way about the problematics of participating in ceremonies. 

James Parker (00:54:40) - Um, that book by Bernard Harcourt where he talks about expository power and, uh, you know, it does seem like the power to not exactly force, but to promote exposure of oneself, you know, whether auditory or otherwise is an increasingly important front politically. 
