# Mapping the Landscape of Independent Food Delivery Platforms in the United
States

Yuhan Liu<EMAIL_ADDRESS>0000-0001-6852-6218 Princeton UniversityUSA ,
Amna Liaqat<EMAIL_ADDRESS>0000-0002-5170-1945 Princeton UniversityUSA
,  Owen Xingjian Zhang<EMAIL_ADDRESS>0009-0008-2949-7379 Princeton
UniversityUSA ,  Mariana Consuelo Fernández Espinosa<EMAIL_ADDRESS>0009-0004-1116-2002 University of Notre DameUSA ,  Ankhitha Manjunatha
<EMAIL_ADDRESS>0009-0006-4341-6948 Princeton UniversityUSA ,  Alexander
Yang<EMAIL_ADDRESS>0009-0000-4212-0228 University of MarylandUSA ,
Orestis Papakyriakopoulos<EMAIL_ADDRESS>0000-0003-4680-0022 Technical
University of MunichGermany and  Andrés Monroy-Hernández
<EMAIL_ADDRESS>0000-0003-4889-9484 Princeton UniversityUSA

(January 2023; July 2023; November 2023)

###### Abstract.

Beyond the well-known giants like Uber Eats and DoorDash, there are hundreds
of independent food delivery platforms in the United States. However, little
is known about the sociotechnical landscape of these “indie” platforms. In
this paper, we analyzed these platforms to understand why they were created,
how they operate, and what technologies they use. We collected data on 495
indie platforms and detailed survey responses from 29 platforms. We found that
personalized, timely service is a central value of indie platforms, as is a
sense of responsibility to the local community they serve. Indie platforms are
motivated to provide fair rates for restaurants and couriers. These
alternative business practices differentiate them from mainstream platforms.
Though indie platforms have plans to expand, a lack of customizability in off-
the-shelf software prevents independent platforms from personalizing services
for their local communities. We show that these platforms are a widespread and
longstanding fixture of the food delivery market. We illustrate the diversity
of motivations and values to explain why a one-size-fits-all support is
insufficient, and we discuss the siloing of technology that inhibits
platforms’ growth. Through these insights, we aim to promote future HCI
research into the potential development of public-interest technologies for
local food delivery.

gig economy, food delivery, infrastructure

††copyright: rightsretained††journal: PACMHCI††journalyear:
2024††journalvolume: 8††journalnumber: CSCW1††article: 92††publicationmonth:
4††doi: 10.1145/3637369††ccs: Human-centered computing Empirical studies in
HCI

##  1\. Introduction

Food delivery platforms provide the logistically challenging service of
transporting meal orders from restaurants to customers (Kenney and Zysman,
2016; Zwick, 2018), typically coordinating their work through an array of
disparate technical tools. Food delivery platforms have become an invaluable
and permanent part of the United States food and hospitality industry
(Anderson et al., 2021). Three mainstream platforms: Uber Eats, DoorDash, and
Grubhub dominate most regions of the United States (Ahuja et al., 2021). These
mainstream platforms have received considerable attention from scholars and
the media, documenting their rise in popularity and impact on local economies
(Veen et al., 2020; Richardson, 2020). In the shadow of these dominating near-
monopolies, numerous other food delivery platforms operate across the United
States. For years, these platforms have been emerging, growing, failing, and
succeeding, while going unnoticed by researchers. The resilience of these
platforms, despite the competition of the well-funded players, suggests that
they may address unmet needs in the food delivery market. However, little is
known about what platforms exist, why they are founded, their operating
models, and the regions they serve.

Within the food delivery landscape in the United States, the widespread,
complex phenomenon of independent or “indie” platforms is poorly understood.
In this work, we use the term “indie” platforms to refer to food delivery
services that are not one of the “Big Three” (Uber Eats, Doordash, Grubhub).
Scholars have documented the detrimental effects that mainstream platforms
have on other participating stakeholders, e.g., couriers (Saxena, 2019).
However, what role indie platforms play in the food delivery business in the
United States remains unexplored. In this paper, we begin to map the landscape
of independent restaurant delivery services in the United States. We ask the
following research questions:

  1. (1)

What are the motivations and values of indie platforms in the United States?

  2. (2)

What is the size of their operation?

  3. (3)

What technical infrastructures do they rely on?

  4. (4)

What are the challenges they face?

We scraped publicly available online sources to collect data on 495 indie
platforms across the United States. We also partnered with the Restaurant
Marketing Delivery Association, also known as RMDA, to survey and gather
detailed responses from 29 indie platforms to complement insights from the
scraped data. In the survey, we asked platforms to describe their
organizational history and structure, such as their values, software
infrastructure, and vision for the future.

Through this investigation, we aimed to provide researchers with a description
of indie platforms. We learned that indie platforms serve as viable
alternatives to the existing mainstream food delivery platforms. However, most
of their primary emphasis lies in catering to the local community they engage
with. We found that these indie platforms are operated largely on a local
level, and they heavily rely on infrastructures designed and built by third-
party vendors, which bring obstacles to supporting their communities as they
intended. We discuss how existing technical limitations create a disconnect
between how indie platforms would like to operate and their current processes,
and we propose how social computing researchers can address this gap. We also
identified operating challenges indie platforms face, such as a lack of
couriers, a low volume of orders, etc.

In summary, we make two major contributions. First, we map the landscape of
independent food delivery platforms in the United States to provide previously
unavailable, aggregated insights. Second, we identify opportunities for HCI
researchers to support indie platforms in creating sustainable, locally
operated alternatives to mainstream platforms.

##  2\. Related Work

The food delivery industry has experienced significant changes in the past
decade. Food delivery transitioned from the old model reliant on customers
contacting restaurants directly, e.g., calling pizza parlors or Chinese
restaurants to get delivery, to online food delivery platforms (Hirschberg et
al., 2016). Online food delivery platforms coordinate the request,
preparation, and delivery of food by connecting restaurants, couriers, and
customers through their applications. They aggregate supply and demand in the
regions they serve (Kenney and Zysman, 2016; Zwick, 2018). Several research
studies have focused on understanding the dominant online food delivery
platforms such as Grubhub, Uber Eats, and Doordash (Griesbach et al., 2019;
Barratt et al., 2020). Specifically, researchers have investigated their
business practices (Netzer et al., 2017; Gondek, 2021), the technological
tools they use (Egg, 2021), and the power relations among platforms,
restaurants, consumers, and workers (Asdecker and Zirkelbach, 2020).
Nonetheless, little attention has been given to indie food delivery platforms
that have different operational, technical, and organizational features (Dalal
et al., 2023).

In this section, we present these two categories of food delivery
platforms—mainstream and independent—and give an overview of distinct features
that previous research studies have uncovered. First, we discuss the
mainstream presence of large platforms, and we describe their business model
and operations. We touch on the shortcomings and gaps of mainstream platforms,
despite their dominating presence in many regions. Furthermore, we look at how
researchers have approached them as a sociotechnical phenomenon. As we
demonstrate, little is known about their operations, business values, and
technical infrastructure. Our study contributes towards this direction,
generating important knowledge on their technical and operational
characteristics, the values they possess, as well as how they distinguish
themselves from the larger mainstream platforms.

###  2.1. Mainstream Food Delivery Platforms: Features & Issues

The United States food delivery landscape in 2022 is dominated by three large
platforms: Uber Eats, Doordash, and Grubhub. Together, the “big three”
dominate the United States with over 90% collective market share (Ahuja et
al., 2021). We term these three platforms “mainstream” because of their
widespread recognition and large market share, and the similarities in their
origin, growth, and operations (Thompson, 2020; van Doorn and Badger, 2020;
Kenney and Zysman, 2016). All these three mainstream platforms experienced
enormous growth during the past decades by merging with competitors (Zhang,
2021), using exploitative tactics to coerce restaurants to join and stay on
the platform (Tkacik, 2020) and recruiting a large number of couriers and
leveraging state of art algorithms for dispatching to optimize delivery speed
and take as many orders as possible (Gondek, 2021). The large scale and
operating model of the mainstream platforms creates a large power asymmetry
between them and other stakeholders, especially restaurants and couriers
(Gondek, 2021; Yao et al., 2021). Given the concentrated power that mainstream
platforms possess, recent studies in the CSCW and HCI communities have
explored harms associated with them, such as harassment and bias towards
vulnerable gig workers, and information asymmetries in algorithm management
(Tuco et al., 2021; Ma et al., 2022; Kusk and Nouwens, 2022; Ma et al., 2023).

Furthermore, researchers have described how the needs of restaurant owners and
couriers are not reflected in the design of food delivery platforms (Zuboff,
2015; Li et al., 2022; Semaan, 2019; Friedman et al., 2013; Saxena, 2019). For
instance, mainstream platforms’ high commission rates cut into the earnings of
already struggling local restaurants. Restaurants are hesitant to raise prices
because doing so may drive away price-sensitive consumers (Machi, 2021a;
Thompson, 2020). This puts participating restaurants in a situation where they
must take a hit on their profit margins to avoid potentially driving away
consumers. These negative impacts have driven some restaurants and couriers to
explore alternative options for participating in the food delivery platform
economy. These alternatives comprise approximately 5-10% of the market that is
not controlled by mainstream platforms (Ahuja et al., 2021). In our work, we
aim to uncover who these unknown “other” players are that continue to exist in
a highly competitive market. In the next section, we describe what is known
about these smaller platforms and what remains to be investigated.

###  2.2. Indie Platforms as an Alternative: An Understudied Sociotechnical
Phenomenon

Similar to other industries where indie is defined by what is not mainstream
(Newman, 2011; Lipkin, 2013), any platforms that are not Uber Eats, Doordash,
or Grubhub and provide food delivery services for more than one restaurant are
broadly categorized as ”indie” platforms in this study. No prior research has
been conducted to explore indie platforms as a whole. Certain case studies
provide us with a solid starting point. Dalal et al. conducted a case study
regarding an indie platform in Colorado. The previous study has demonstrated
the importance of human intervention when addressing infrastructure breakdown
and provides us with insights into this emergent sociotechnical phenomenon
from different stakeholders’ perspectives (Dalal et al., 2023). However,
little empirical work has been done to detail a fine-grained and integrative
understanding of what the indie platform landscape looks like in the United
States. We do not know how many of these indie platforms exist, the scale of
their operations, the areas they serve, the technological shortcomings they
face, and how they sustain and grow in a highly competitive landscape. Thus
far, academic research into these new models has been limited, often only
focusing on one subset that functions as co-operatives (Bunders et al., 2022;
Scholz and Schneider, 2016; Schor, 2021).

In this paper, we aim to map the indie platform landscape, uncover aggregated
insights into the operations, scales, challenges, and needs of these
platforms, and provide direction for CSCW and HCI seeking to better understand
indie platforms and the local communities they serve. We thereby initiate a
preliminary exploration of the indie online food platform ecosystem.,
utilizing the online presence of these indie platforms and their organization
in associations such as the RMDA. Adopting a mixed-method approach, we
combined a large-scale data-driven analysis and small-scale structured
surveys; we describe the indie platforms’ motivation and values, operational
features, technical infrastructure, and challenges in the following sections
of this paper.

##  3\. Method

We composed a list of 495 U.S.-based indie platforms by aggregating data from
news sources, automated web searches, mobile app marketplaces, and
collaborations with indie platforms. We then scraped information from their
websites and surveyed their operators. We explain these processes below.

###  3.1. Building a List of Indie Platforms

We gathered a list of 495 indie platforms from the following sources and
removed duplicates:

  * •

Seed list. We obtained a list of 30 platforms from the co-founder of an indie
platform we had collaborated with.

  * •

News articles. We extracted a list of 89 platforms from news articles
published from January 2015 to April 2022 from the Media Cloud 111Media Cloud
is a constantly growing open-source collection of global news on the open web
(Roberts et al., 2021) maintained by academic researchers archive using the
keywords “local food delivery service” in their API. Two of the authors read
the articles and identified the U.S. platforms.

  * •

Mobile Apps. We identified 259 platforms from the Google Play Store. We
searched for apps published by the same publisher of the platforms we had
collected already. For example, the app for Nosh, a platform in Colorado we
had in our list, was published by Data Dreamers, a software provider that had
published dozens of apps for other platforms. Note that we only collected data
from the Google Play Store because the Apple App Store would have produced an
incomplete list 222The Apple App Store limits the number of apps uploaded by
the same publisher, hence searching by publisher would result in an incomplete
list. Publishers seem to circumvent those limitations by creating multiple
accounts, which we could not confidently retrieve.

  * •

Web search. We retrieved 194 platforms using the Google search API. We
searched for local food delivery services in all U.S. towns with a population
over 50,000333We obtained a list of towns from simplemaps.com. We used the
town names to iteratively collect the first 50 search results with a query
that excluded the mainstream platforms and unrelated services, such as meal
kit companies and grocery delivery:

        local food delivery city -uber -grubhub -doordash -meal -grocery -farm -courier

These search results still returned irrelevant websites. Members of our
research team manually analyzed the websites and extracted names and websites
of relevant platforms.

  * •

Trade organization. We obtained a list of 230 platforms from the Restaurant
Marketing Delivery Association (RMDA), a non-profit that connects restaurant
delivery services with one another and organizes a yearly conference. The
RMDA’s website has an interactive map that includes all their members in the
US.

###  3.2. Mapping the Geographical Spread of Platforms’ Operations

To map the geographical areas where a platform operated, we collected data on
the restaurants available on each platform. We extracted the addresses of
116,855 restaurants from the 328 platforms that were available on the web and
their publicly shared lists of cooperating restaurants. Restaurant data
included name, address, and contact information.

Using the topological maps provided by the census bureau (Bureau, 2022), we
located the boundaries of zip codes in which served restaurants were located
for each platform. Then, by using algorithms 1 and 2 (outlined in Appendix A),
we aggregated neighboring zip codes. Algorithm 1 greedily assigned the zip
codes of restaurants to clusters based on neighboring adjacency (i.e., shared
common borders), while Algorithm 2 merged clusters that contained common zip
codes. In this way, we were able to define how many non-regionally adjacent
clusters each platform served. For example, a platform serving a small town
would usually serve one cluster, while a platform that would have regional
coverage of multiple towns would have more. This clustering approach served as
a proxy to indicate the geographical spread of a platform’s operations.

###  3.3. Survey Instrument

To gain further insight into the operations of indie platforms, we developed
and deployed an online survey with 31 questions, divided into six sections:
Platform Overview, Digital Infrastructure, Restaurant and Customer
Relationship, Future Vision, Financial Status, and Open questions. Questions
were a mix of multiple-choice and open-text responses.

We contacted platforms directly using the email listed on their website or via
the RMDA mailing list. The email included an overview of our research project,
its goals, and a link to the survey. We invited owners and managers of
delivery platforms to complete a short, 25-minute survey on their experiences
with building, maintaining, and growing their organizations. The email stated
that survey responses would be kept confidential. Respondents could opt to
enter a raffle for a $100 Amazon gift card. The open-ended responses were
coded by two researchers (the first and second authors), following the
iterative approach proposed by Braun and Clarke (Braun and Clarke, 2006). The
researchers independently coded a subset of the responses and then discussed
to reach an agreement on the combined codebooks. These codes were then grouped
together into broader themes through a thematic analysis.

The Princeton University Institutional Review Board approved this work. Survey
participants were not obligated to answer every question. The number of
responses collected corresponding to each question is listed in Appendix B.

##  4\. Findings

In this section, we answer our four research questions. First, we present
several themes that encompass the motivations and values of indie food
delivery platforms (RQ1). Next, we describe the size of operations of indie
platforms with five different patterns of geographic distribution we observed,
along with their weekly order volume (RQ2). We then summarize what we learned
about the technical infrastructures used for running their operations (RQ3).
Lastly, we discuss the challenges that indie platforms encounter in expanding
their presence in a highly competitive market (RQ4).

###  4.1. RQ1: What Are the Motivations and Values of These Platforms?

The following themes emerged from the websites’ analysis and the survey
responses:

####  4.1.1. Owners belong to the community they serve

Many owners of indie delivery platforms have lived in the community where they
operate for many years. These kinship ties with their customers, employees,
and local restaurants fostered feelings of responsibility and commitment for
platform owners.

Owners expressed a commitment to giving back. We found that many platforms
publicly share how they give back to the communities where they operate. Ways
of giving back include donating a portion of profits to local charities and
volunteering. For example, owners wanted to allow orders to be rounded up for
local charities.

> “During checkout you will be given the option to round up for charity and
> these proceeds will go directly to Seek and Find Ministries444A local
> Christian charity organization to help support their mission.” (Swiftly,
> Illinois)

> “At Kershaw2Go, we believe in supporting our community through interactions
> and donations. Each quarter, we pick a local non-profit based in Kershaw
> County and sometimes in surrounding areas and donate towards their cause.”
> (Kershaw2Go, South Carolina)

We also found that owners identify with cause or group. Many platforms have a
strong sense of identity. We found that many platforms proudly promote
ownership identity, which includes being owned by women, veterans, or
Christians. Black and Mobile (Philadelphia) explains how identity shapes their
business values:

> “Black and Mobile is the country’s first Black Owned food delivery service
> that exclusively partners with Black-Owned restaurants to give them more
> exposure and customers.” (Black and Mobile, Philadelphia)

In the survey responses, platform owners expressed that locally owned and
operated businesses have close ties to their community and a personal
responsibility to give back. All respondents described a locally owned and
operated business where owners are active within the community. This includes
involvement in the daily operations of the business and working “on the
ground,” as well as community participation outside of their business. One
owner explained that they do not believe in correcting errors through giving
credits, as is the default with mainstream platforms:

> “We try to go the extra mile for our customers. If something is missing, we
> always try to go back and get it and not just give a credit.” (S3)

Other ways that locally owned businesses distinguish themselves is through
serving customers and restaurants that are overlooked by mainstream platforms:

> “Delivery apps in [location of operation] are restricted to a specific
> delivery radius and deliver either pizza or fast food.” (S11)

Owners also described how they give back to their community:

> “In 2020 we bought over $10,000 in Christmas gifts for families in the
> community” (S3)

All indie platform owners detailed the ways in which they serve their
communities through active citizenship, beyond simply providing a food
delivery service. This commitment to ensuring fair profit-sharing between all
parties, quick access to human support when errors occur, and charitable
contributions distinguished indie platforms from their competitors.

####  4.1.2. Motivated to provide better price or experience than existing
platforms.

Indie platforms believed they offered competitive advantages that existing
platforms, including larger competitors, did not. One of these advantages was
charging low commission rates. Mainstream platforms charge high commission
fees that make it hard for restaurants to sustain their business. Some indie
platforms were motivated to offer food delivery at affordable rates:

> “We offer something that apps like Postmates don’t — support and lower
> commission fees.” (Foodie Magoo’s, Arizona)

> “We saw local restaurants getting price gouged by the national delivery
> services, and we wanted to offer a better price and program for restaurants.
> On that same note, national delivery services take 30% from restaurants, and
> 10-15% from customers, and then pay drivers a low city minimum
> ($2.50/order), and they keep the remainder of the profits. We wanted to
> provide restaurants and drivers both a better opening share of the
> proceeds.” (S7)

In the survey, platforms described how they charged commission fees. Out of
the twenty-six responses we collected, half of the platforms charge fixed
percentages ranging from 3% to 40%. 27% of the indie platforms have a sliding
model, such as different rates for affiliated and non-affiliated restaurants.
One platform charges a flat $2, while the rest of the 19% platforms do not
charge any commission fees.

Indie platforms also have the advantage of promoting specialty cuisine. Some
indie platforms provide the unique specialization of catering to a certain
cuisine or dietary lifestyle. These include platforms that accept only locally
owned restaurants, platforms for restaurants that are Halal or Kosher, or
platforms that specialize in Asian or African cuisine. Platforms are motivated
to elevate their community’s food scene, which is consistent with the feelings
of connection many platform owners have for their communities. Describing
their commitment to staying local, Chopchop states:

> “Chris Chandler wants to take food-delivery service out of the hands of the
> big corporations and provide local service, using a local delivery team,
> with a local touch” (Chopchop, Virginia)

In the survey responses, platform owners revealed that timely, personalized
human involvement differentiates indie platforms. When asked what
differentiates their business from competitors, seventeen of the 29
respondents commented on the personalized service they provide and the ease
with which they could be contacted. Seven platforms mentioned keeping profits
local and providing better rates for drivers and restaurant owners. Platform
owners commented that being local allows for immediate, personalized responses
to issues when they arise:

> “If there’s a problem, they call me, the owner of [my platform]. If they
> need to make a change, they can text, call, or chat with me while I’m
> picking up an order or eating dinner. I’m not in an office 1200 miles away.”
> (S5)

> “We have people that answer the phone that live in the community so can
> relate to places and times.” (S26)

This allows them to provide better service, with a platform owner sharing that
they outperform the mainstream competitor in their area through faster
service, resulting in restaurants leaving the mainstream platform to join
their platform. Platform owners compared their business approach to other food
delivery business models:

> “(We have) Much higher Service Standards, We are Local, We are personal. Our
> larger competitors don’t do redeliveries when items are missing. We do. Our
> larger national competitors do not accept cash as a form of payment - We
> do.” (S26)

> “We are customer oriented. We have call center services and resolve issues
> directly with our customers in real-time. None of these money-losing app
> competitors can fix a delivery problem – missing item, wrong item,
> unsatisfactory item, etc. We can get drivers back at restaurants and do re-
> trips if needed. Also, unlike the money-losing apps, we can offer instant
> credits or refunds.” (S27)

####  4.1.3. Feel strongly about revitalizing local economies.

Indie platforms were created because founders wanted to support their local
economy by creating more job opportunities and keeping profits looping within
their local communities. Many platforms advertise that they employ locally, or
that they partner with local restaurants. They believe this allows them to
provide more personalized, attentive service to customers as they can easily
adjust orders. Through their direct connections with restaurants, they can
better understand business owners’ needs.

In our study, we analyzed indie platforms’ collaboration with chain and local
restaurants. We used the chain restaurant names listed on Wikipedia to match
our data (Wikipedia contributors, 2023) and calculated the percentage of chain
restaurants listed on each platform’s website. The counts are shown in Figure
1. We noticed that more than half of indie platforms(176 out of 326) have less
than 20% chain restaurants listed on their website, while the average
percentage of chain restaurants listed on indie platforms is 22.16%. Note that
50 platforms exclusively partner with local restaurants (no chain restaurant
is listed on their website).

Figure 1. Percentage of chain restaurants on indie platform websites

Platforms also aim to keep money local. since they are locally owned. This
sometimes means that the owners live and work where their platform operates.
In other cases, the restaurant owners on a platform also have shares in the
platform itself. Through these locally owned and operated business models,
indie platforms help to circulate the profits of their success within their
local communities.

The survey responses shed further light on how indie platform owners were
motivated to create equitable business models in under-served areas. Four
platform owners (13.8%) saw a business opportunity for food delivery, with
three platforms specifying that there were no delivery services in the small
towns where they started their businesses. Two platforms were motivated by
personal reasons (e.g., leaving a career due to health challenges). Five were
motivated to start their business out of a desire to create a locally-owned
delivery service. These platforms emphasized care, support, and giving back as
central values to their business. One platform owner, who was formerly a
driver for a mainstream platform, shared their story:

> “I was driving for GrubHub, and a local restaurant owner wanted to offer
> delivery, but the big 4555The respondent is referring to Uber Eats, Grubhub,
> Doordash, and Postmates. In this study, we call it the “big three” because
> Postmate was acquired by Uber in 2020.  weren’t interested. I started [my
> platform] to offer local restaurants the same benefit of a shared delivery
> service that the national chains had.” (S5)

Indie platforms expressed their commitment to serving their local community
and developing a business model that benefited drivers, restaurant owners, and
customers. This motivation often arose after witnessing the impact of
mainstream platforms on their community, as described by one platform owner:

> “We saw local restaurants getting price gouged by the national delivery
> services, and we wanted to offer a better price and program for restaurants.
> On that same note, national delivery services take 30% from restaurants, and
> 10-15% from customers, and then pay drivers a low city minimum ([In the city
> where the platform operates] it is $2.50/order), and they keep the remainder
> of the profits. We wanted to provide restaurants and drivers both a better
> opening share of the proceeds.” (S7)

###  4.2. RQ2: What Is the Size of Operation of These Platforms?

After uncovering the presence of hundreds of small food delivery platforms, we
investigated the size of these operations. Through mapping the restaurant’s
zip codes, we identified clusters where each platform operates. We found a
range in the size of operations, from platforms localized to a single
geographic region, to multi-state operations. The variety in geographical
dispersion of indie platforms suggests that there are likely diverse
motivations, goals, and values that drive their expansion (or lack of it). The
three mainstream platforms operate nationwide, with each one offering food
delivery in every state across the United States. Though none of the indie
platforms operated on the same size as mainstream platforms, we did find that
all states but one (Vermont) had an indie platform presence. Below, we list
the five categories of operation, from the most geographically concentrated to
the most widespread:

  1. (1)

One cluster. A platform operating in one cluster serves restaurants that cover
one contiguous area. We found 153 (46.65%) platforms that operate in a single
cluster. For example, Figure 2(a) is an indie platform operating in
Ellensburg, WA.

  2. (2)

Multiple clusters within a state. Some platforms have restaurants in multiple
clusters within a single state. This means the platform serves zones that are
geographically separated. We found 97 (29.57%) platforms that operate in
multiple clusters within a state. For example, Figure 2(b) shows an indie
platform operating in Ideal and Savannah, GA.

  3. (3)

Multiple clusters across neighboring states. Similar to the preceding
category, platforms in this category have a presence in geographically
distinct zones, though they are in more than one state. However, all states
they operate in share a border, suggesting some geographical proximity between
clusters. We found 20 (6.1%) platforms in this category, as shown in. For
example, Figure 2(c) shows an indie platform operating around the border of
Kansas and Missouri.

  4. (4)

Multiple clusters across non-neighbouring states. Platforms in this category
also have multiple clusters across states like the prior category. However, a
platform in this category operates in states that do not share a border,
suggesting a geographical distance between clusters. We found 48 (14.63%)
platforms in this category. For example, Figure 2(d) shows an indie platform
operating in Nelsonville, OH, and Athens, GA.

  5. (5)

Nationwide. A handful of platforms operate in many states across the United
States (i.e., more than 10 states). We categorized these platforms as
“Nationwide” to distinguish them from the prior category, where a platform
operated in no more than 10 states. Ten (3.05%) platforms operate nationwide
as shown in Figure 2(e).

(a) One Cluster

(b) Multiple clusters within a state

(c) Multiple clusters across neighbouring states

(d) Multiple clusters across non-neighbouring states

(e) Multiple clusters nationwide

Figure 2. Examples of indie platforms’ operation

Besides mapping zip codes to figure out the geographic areas indie platforms
cover, we also asked questions regarding the size of operation in our survey
to indie platform owners. We received 22 responses to those questions. We
found that most of the indie platforms are operating on a small size from the
perspective of weekly order volume. In the survey, we asked how many orders
the platform received the week before filling out the survey. Excluding one
platform that did not receive any orders “last week,” 27% of platforms in our
survey indicated receiving less than 100 orders “last week,” while 27% of the
platforms received 100-200 orders last week. 32% platforms received 200-100
orders “last week” while 14% platforms received more than 1,000 orders the
week before filling the survey. We found that the order volume is a good
indicator to show the size of the platforms as it is highly correlated with
other metrics like the number of active customers, the number of couriers
doing delivery, and the number of restaurants selling food. For example, all
platforms with more than 500 orders have 35-40 couriers for delivery while
most of the platforms with less than 200 orders have less than 10 couriers
doing delivery the week before the survey.

###  4.3. RQ3: What Technical Infrastructures Do Indie Delivery Services Rely
On?

Through aggregating publicly available data on the app stores, we identified
the software and platforms used by indie services. Analysis of survey
responses provided descriptive insight into platform owners’ concerns with the
software they use and software limitations.

Platforms rely on one, or a combination, of three technical infrastructures
for menu listing and ordering: website, iOS app, and Android app. All
platforms, except for four, have websites. Those four platforms without
websites only have iOS apps. In terms of mobile apps, nearly 70% of indie
platforms have published both iOS and Android apps, while 21% of have no
mobile app. Fourteen platforms only published iOS apps and twenty-seven
platforms only published Android apps.

We learned that most platforms did not develop their own software from
scratch. Instead, they relied on third-party companies, like Delivery Logic
and Data Dreamers. These third-party developers provide a suite of software to
handle delivery logistics, including menu-listing software, menu-uploading
software for restaurants to manage stock, and software for delivery drivers to
handle orders and communicate with dispatchers.

While seven platform owners (26%) expressed their satisfaction with third-
party software, twenty survey responses (74%) revealed platform owners’
dissatisfaction with it. They expressed dissatisfaction with the software
limitations, describing a need for greater control and customizability. This
included a need for improved customer UI and UX (S4, S6, S15, S18, S21, S22),
enhanced communication features with drivers and customers (S3, S5, S10, S13),
integration of front and backend (S7, S20), accurate delivery times (S3), and
software that scales (S1). As reported by one platform owner:

> “(We want) The ability to expand more beyond a local area. The software
> isn’t built for large-scale businesses. There’s no way to separate the time
> zones up for different time zones or any of that.” (S12)

Most of these respondents who used third-party software feel that the software
did not meet their unique needs. For example, Data Dreamers does not allow the
platform owner to upload images of food, which creates unnecessary complexity
for the platform:

> “We can’t upload images of food ourselves; the customers can when they get
> their food, and we can pay them for their images as a rewards program. The
> images we approve, no matter how good we crop them, end up totally skewed on
> mobile devices, so it’s pretty wacky.” (S1)

These functional limitations impede the quality of services these businesses
can provide as well as their growth and ability to compete with larger
competition, with one platform owner stating:

> “You would think they [third party software] want the opportunity to help
> grow some national brands. Ability to change more on the software itself if
> we wanted to, but there isn’t a way. The outdated look of the software is
> good for those who like more simple software but does look more old school
> than the larger competition.” (S1)

However, despite the problems platform owners face with third-party software,
many of them are dependent on it. Lack of technical skill (twelve platforms)
and cost (nine platforms) were the two reasons for selecting an off-the-shelf
solution. Hiring engineers to develop and then maintain customized software
was cost-prohibitive, as described by two platform owners who explored a
custom solution but settled on Data Dreamers and Cartwheel:

> “Building software is expensive. I talked to a company to build it for us,
> and it would cost around $180,000-$200,000 for them to develop it.” (S7)

> “Small companies like mine without any private funding can not afford third-
> party developer software to create a custom make solution.” (S29)

###  4.4. RQ4: What Are the Challenges Indie Platforms Face in Their Current
Situation?

From the survey responses we collected, we learned the challenges indie
platforms encountered and how they do or plan to tackle these obstacles. The
challenges are closely related to the financial situation and the size of
these platforms. Thus we include that information together in this section.

The most common challenge platforms face is a lack of couriers (17 out of 24),
followed by a low volume of orders (10 out of 24), a lack of funding (8 out of
24), a shortage of restaurants (7 out of 24), too many delivery services and
not enough customers (5 out of 24), and too many restaurants want to sign up
(2 out of 24). One platform faced the challenge of a shortage of employees. No
platform indicated they have an oversupply of couriers.

In order to better understand the challenges, we asked detailed questions
about how the platform is doing financially. Fourteen out of twenty-two survey
responders mentioned making a profit in the last quarter, one broke even, and
seven did not make a profit.

We also observed that the poor financial situation and lack of couriers are
reasons for platforms shutting down in our scraped data. As U.V.E.R, and
Somerset Delivery stated on their website:

> “The service never broke even, required more and more investment to stay
> alive and the path to cover our costs became too far to reach. After
> contributing tens of thousands of dollars and several hundred hours of
> volunteer time, the founders and board members simply cannot justify keeping
> it alive as a legitimate standalone business.” (U.V.E.R, New Hampshire)

> “We have spent weeks being unable to keep reliable drivers staffed, which is
> causing our location to be closed the majority of our available hours. We
> think it’s time to throw in the towel and shut down for good.” (Somerset
> Delivery, Kentucky)

In fact, we found 128 platforms no longer active 666A platform was classified
as active if it had a functioning shopping cart and checkout. among 495
platforms. Other reasons account for shutdown include acquisition by larger
competitors, e.g., Delivery.com, Bite Squad, or Grubhub (resulting in platform
websites redirecting to the parent platforms’ website), merging with local
competitors (resulting in a re-branding), temporarily closed due to technology
upgrades, and unknown reasons.

Despite facing similar or different challenges, all platforms had plans to
expand in some way. The most common goals in the next year for the platforms
we surveyed were:

  1. (1)

Partner with more restaurants (22 platforms).

  2. (2)

Advertise to acquire new customers (20 platforms).

  3. (3)

Recruit more couriers (20 platforms).

  4. (4)

Expand business to another location (13 platforms).

  5. (5)

Add a service such as catering or alcohol delivery (10 platforms).

  6. (6)

Becoming a delivery provider, i.e., contracting delivery services to third
parties (7 platforms).

  7. (7)

Hire more employees (5 platforms).

##  5\. Discussion

In this section we reflect on indie platforms and what they reveal about their
technology use and their technical needs. We organize the Discussion around
three takeaways. First, we discuss how our findings suggest that indie
platforms are a widespread and longstanding fixture of the United States food
delivery industry. However, unlike mainstream platforms, indie platforms
operate largely on a local level. This key difference informs our next
insight, where we explore why a one-size-fits-all approach to technology
cannot address the unique needs of indie platforms. In our third insight, we
discuss how food delivery platform technology is heavily siloed and how this
siloing may impede indie platforms in performing their business operations and
maintaining their values.

###  5.1. Indie Platforms Are a Widespread Fixture of the United States Food
Delivery Landscape

Our analysis indicated that indie food delivery platforms are a significant
player in the United States food delivery service. Indie platforms are
widespread, existing in all but one state, and have existed for many years,
with hundreds still actively operating. We found that most commonly, platforms
operate within a single geographical area, followed closely by platforms that
operate in multiple geographically separated areas within a state. Most indie
platforms are small operations localized within one or a handful of
communities, which distinguishes them from mainstream platforms that
aggressively pursue expansion into new regions (Schor, 2021).

Mainstream platforms appeal to consumers’ demand for low-cost food delivery by
charging high commission rates to restaurants rather than the end customer
(Thompson, 2020). However, indie platforms continue to maintain a presence
despite the cost-cutting strategies employed by well-funded mainstream
platforms (Machi, 2021b). The existence of hundreds of indie platforms we
discovered suggests that, as similarly reported in prior work, indie platforms
position themselves as alternatives to mainstream platforms and are filling
gaps in the landscape that are unmet by mainstream platforms (Atkinson, 2021;
Machi, 2021b). In our study, we found that indie platforms adopted a variety
of strategies to build presence and trust within the communities where they
operate and that it may be these alternative operating models that keep indie
platforms in the business. The smaller scale of indie platforms in our study,
as well as the community-focused strategies they used, were both key ways in
which indie platforms differed from mainstream platforms. Their survival and
growth in prominence in such a competitive market are largely attributed to
their small-scale, localized nature and human-centered design. In the next
insight, we discuss these differences.

###  5.2. One Size Fits All Does Not Cater to the Human Involvement Aspect of
Indie Platforms

We found that indie platforms centered on human involvement throughout their
business operations (Dalal et al., 2023). Owners engaged with day-to-day
activities, phone operators were easily accessible to quickly resolve issues,
and errors were corrected through personalized service. This person-in-the-
loop approach allowed indie platforms to adapt their services and cater to the
unique needs of a local community, such as only hiring drivers that have been
vetted in person or building a network with local restaurants that mainstream
platforms have rejected. Mainstream platforms that aim to minimize reliance on
human intervention to reduce operating costs and capture larger market share
(Hirschberg et al., 2016; Kelso, 2020) are either uninterested in or unable to
address such specialized needs on a local scale. The resulting harms of such
algorithmic management models are often borne by vulnerable gig workers
(Möhlmann and Henfridsson, 2019). The approaches described by indie platform
owners in our study aimed to alleviate these harms.

The focus on mainstream platforms has linked gig work with economies of scale
in the public eye, promoting a general perception that gig work must entail an
aggressive lowering of costs and increase of production to be profitable
(Sherman, 2019). Researchers have identified similar issues in India and China
(Seetharaman et al., 2021; Anjali Anwar et al., 2022; Chen et al., 2022).
However, our analysis revealed an alternative, viable business model adopted
by indie platforms. There is a large digital divide in rural areas (Duff,
2021). Limited broadband and high-speed internet make human intervention
necessary for facilitating digital transactions (Duff, 2021), and therefore,
rural areas are an unappealing market for mainstream platforms that rely on
economies of scale (Sherman, 2019). Indie platforms in our study filled spaces
that are overlooked by larger platforms, such as by serving small rural areas,
working with small local restaurants rejected by mainstream platforms, and
supporting individuals from under-served communities. From analyzing the
motivations of indie platforms, we found that the creation of these platforms
was often sparked by identifying a need or gap within their community. As a
result, while all indie platforms shared a hands-on philosophy, there was
diversity in their specific values, priorities, and business processes.
Therefore, a one-size-fits-all approach to technical solutions for food
delivery platforms may prevent smaller businesses from engaging with
customers, couriers, and restaurants in a way that aligns with their values.
We discuss the limitations of existing food delivery technology for indie
platforms in the next insight.

###  5.3. Siloing of Technology Impedes Indie Platforms

At the software level, indie platforms use two suites of software to operate
their business: marketplace software for customers, couriers, and restaurants;
and dispatch software that handles the delivery logistics. Like the results
shown in the previous section, most of the indie platforms in this study rely
on third-party software vendors for both types of software.

We found that this reliance on third-party software poses challenges for indie
platforms that differentiate themselves through unique, specialty services.
For example, these third-party software vendors use the same templates to
build websites and apps for different indie platforms, which makes it hard for
platforms to have identifiable impressions through this customer-oriented
software. Moreover, there is a lack of customization. Platforms can edit the
welcome message, instructions for users, etc, but there is no support for
phoning in orders, limitations to setting delivery radius, and insufficient
communication for interacting with couriers. However, indie platforms must
make do with these off-the-shelf solutions as cost and lack of technical skill
prohibit them from developing custom solutions. We discovered a need for
technology that empowers indie platforms to customize operational business
processes, but the siloing of existing software makes that a challenge. Indie
platforms have embraced customers’ demands to shift food delivery beyond a
simple service. Consumers are willing to pay premiums to enhance the food
delivery experience into something fun, and memorable, such as through gourmet
meal kits and high-quality packaging (Gavilan et al., 2021). Furthermore, we
noticed the high financial cost from the third-party vendors for indie
platforms. They take a fixed margin for each order from indie platforms,
ranging from $0.25-$0.99, and some charge additional monthly fees and set-up
fees, which creates challenges for platforms scaling up.

Here, we propose a concrete path for HCI researchers to develop solutions: a
federated, open-sourced framework with a standardized set of protocols. Such
design should enable key information revealing like the pricing schema to
bring customers more space for comparing the service and quality between indie
and mainstream platforms. Furthermore, the framework should include gig
worker-centered design to improve the life of couriers (Ma et al., 2023).
Recent literature on Mastodon has already shown the great potential for
affordable, federated systems to become the alternative to existing powerful
platforms and fill the gap between the demand of users and the design of the
platform (Raman et al., 2019; Zignani et al., 2019). Similarly, in the
federated food delivery network, indie platforms could set up their own
instances and customize UI and UX design based on their needs to serve
different communities and support different stakeholders. Information and data
could flow within the whole network no matter which instance the customer is
hosted on since a standardized set of protocols supports the framework. This
allows customers to access a broader range of choices from various instances
without downloading different apps while preserving their preferred user
experience. In addition, The protocol should give due consideration to data
portability to ensure that stakeholders, such as restaurant owners, have the
ability to control and manage their own data. This aspect can be taken into
account along with data signature mechanisms to address the issue of platforms
listing restaurant menus without proper consent. A similar design has already
been implemented in the “Bluesky” (Graber et al., 2021; Team, 2022). The
federated food delivery network also supports in-network collaboration. For
example, platforms short of delivery labor could forward the order to other
platforms that have extra delivery labor or platforms that are specialized in
delivery (like the delivery service provider mentioned in Section 4.4). Such
collaboration might be difficult to achieve from a business perspective, but
the proposed food delivery network makes the collaboration feasible from a
technical perspective. Such solutions could be low-cost and high-customization
alternatives to the existing food delivery software indie platforms use.

##  6\. Limitations and Future Work

Our survey consisted of 29 responses, and may not be representative of all
indie platform owners. Additionally, the list of indie platforms that we built
represents a first attempt at mapping such platforms in the United States. It
is likely that we overlooked some platforms in our search, and therefore, this
is not an exhaustive investigation into all U.S.-based indie food delivery
platforms. To be more specific, our focus is solely on food delivery platforms
with web and mobile applications, excluding any other platforms that do not
fall within this scope. Moreover, the crawled data is gathered through
platform websites without scraping data from any mobile applications. However,
only 4 platforms in our study don’t have a website, which we believe has a
limited impact on the overall results. Furthermore, the identification of
indie food delivery platforms heavily relied on keywords and geographic
representations. Consequently, there is a possibility that we might have
overlooked platforms that do not prominently feature phrases like “local food
delivery” in their online presence or platforms operating in small cities with
populations below 50,000.

Future research could expand the scope of our work by surveying other
stakeholders involved in indie food delivery, such as restaurants, couriers,
and customers. In our work, we identified the limitations of off-the-shelf
solutions. Future work could further investigate the technical needs of indie
platforms and the design of open-source software to offer low-cost
customizability for indie platforms. Furthermore, studies should focus on the
perceptions of consumers towards indie food delivery platforms, in order to
understand which factors can contribute to platforms’ consumer base growth and
sustainable presence in the food delivery space.

##  7\. Conclusion

Indie platforms have been a relatively unexplored player in the food delivery
landscape. In this work, we aimed to start mapping the hundreds of indie food
delivery platforms across the United States and to investigate why and how
they operate. We found that many of these platforms have been in operation for
several years, and continue to grow through location expansions, customer
acquisition, and restaurant partnerships. Indie platforms are committed to
their vision of “being local,” which may look like collaborations with under-
served independent restaurants, donating to charities, and hiring local
couriers to keep profits within their community. We found that indie platforms
adapt their services to provide timely, personalized service to their
stakeholders, such as through phone ordering and easily accessible live
customer support. Cost constraints many platforms to off-the-shelf solutions,
which lack the customizability for platforms to agilely respond to
stakeholders’ needs.

Our study presented three implications for future research and investigation.
We showed that indie platforms are a widespread fixture of the food delivery
landscape, that the diversity in their motivations, needs, and values makes
one size fits all solutions inadequate, and that the silos created by the off-
the-shelf solution impede their operations. Our survey revealed that being
local allows indie platforms to not only provide improved quality and speed of
service, but potentially better rates than mainstream platforms. Indie
platforms are in a period of transition as they plan to grow and expand, and
are in need of flexible software that can meet their evolving needs.

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##  Appendix A Clustering algorithms

Algorithm 1 Greedy restaurant clustering
The algorithm iterates over the zipcodes served by a platform and checks
whether they have common boundaries. If yes, it assigns them to the same
cluster.

1:$\texttt{list\\_of\\_zipcodes}\leftarrow
get\\_unique\\_zipcodes(restaurants_{platform})$

2:$\texttt{list\\_of\\_clusters}\leftarrow NULL$

3:for new_zipcode in list_of_zipcodes do

4: if list_of_clusters is NULL then

5:
$\texttt{list\\_of\\_clusters}.append(\texttt{new\\_cluster(new\\_zipcode))}$

6: else

7: $\texttt{assign\\_zipcode\\_to\\_cluster}\leftarrow FALSE$

8: for cluster in list_of_clusters do

9: for zipcode in cluster do

10: if zipcode $\neq$ new_zipcode then

11: if is_adjacent(zipcode,new_zipcode) then

12: $\texttt{cluster}.append\texttt{(new\\_zipcode)}$

13: $\texttt{assign\\_zipcode\\_to\\_cluster}\leftarrow TRUE$

14: break

15: end if

16: else

17: $assign\\_zipcode\\_to\\_cluster\leftarrow TRUE$

18: end if

19: end for

20: end for

21: if assign_zipcode_to_cluster is FALSE then

22:
$\texttt{list\\_of\\_clusters}.append(\texttt{new\\_cluster(new\\_zipcode))}$

23: end if

24: end if

25:end for

Algorithm 2 Merge clusters
The algorithm iterates over clusters created by algorithm 1 and checks whether
they share common zipcodes. If yes, it merges them to the same cluster.

1:$\texttt{list\\_of\\_merged\\_clusters}\leftarrow NULL$

2:for cluster in list_of_clusters do

3: if list_of_merged_clusters is NULL then

4: $\texttt{list\\_of\\_merged\\_clusters}.append(\texttt{cluster)}$

5: else

6: $\texttt{Merged}\leftarrow FALSE$

7: for new_cluster in list_of_merged_clusters do

8: if have_common_zipcodes(new_cluster, cluster) then

9: $\texttt{new\\_cluster}\leftarrow merge(\texttt{new\\_cluster, cluster})$

10: $\texttt{Merged}\leftarrow TRUE$

11: end if

12: end for

13: if Merged is FALSE then

14: $\texttt{list\\_of\\_merged\\_clusters}.append(\texttt{cluster)}$

15: end if

16: end if

17:end for

##  Appendix B Number of Responses Collected of each question in the Survey

Table 1. Number of Responses Collected of each question in the Survey

Question | Number of Responses
---|---
When was your platform founded? | 29
What motivated you to create your platform? | 29
In what locations does your platform operate? | 29
What distinguishes your platform from other similar services in the same area? | 29
Who built your platform’s marketplace (frontend) software? | 29
Who built your platform’s dispatch (backend) software? | 28
What would you like to change about your platform’s software, frontend and backend? | 26
You mentioned using self-built software, why did you decide to build the software yourself instead of buying from a third-party company? | 3
You mentioned using software built by another company. Why did you buy that software from a third-party company instead of building it yourself? | 25
What commission fee does your platform charge restaurants? | 26
How does your platform get feedback from restaurants? | 26
How does your platform act on feedback from restaurants, if at all? | 25
What are common frustrations customers encounter when ordering food on your platform? | 25
How does your platform handle those customers’ frustrations? | 24
How do customers contact your platform when they have problems or suggestions? | 25
How does your platform respond to customers’ problems or suggestions, if at all? | 25
When problems involve costs, who covers those costs? | 25
Which of the following challenges is your platform facing? | 25
What are the plans for your platform for the next 12 months? | 25
How could policymakers help your platform tackle the challenges it faces, if at all? | 23
How many orders did your platform process last week? | 22
How many restaurants sold food on your platform last week? | 22
How many people ordered food on your platform last week? | 22
How many couriers, e.g., drivers, were delivered for your platform last week? | 22
Financially, how did your platform do last quarter? | 22
What does “locally owned and operated” mean to you? | 22
What differentiates a “locally owned and operated” restaurant delivery service from other restaurant delivery services? | 20


# Generating Diverse Realistic Laughter
for Interactive Art

M. Mehdi Afsar
Mila & University of Calgary
Eric Park11footnotemark: 1
Mila & University of Waterloo
Étienne Paquette
Independent Artist
Gauthier Gidel
Mila & University of Montreal
Kory W. Mathewson
DeepMind
Eilif Muller22footnotemark: 2
Mila & University of Montreal
Equal contribution, corresponding author<EMAIL_ADDRESS>CIFAR
AI Chair

###### Abstract

We propose an interactive art project to make those rendered invisible by the
COVID-19 crisis and its concomitant solitude reappear through the welcome
melody of laughter, and connections created and explored through advanced
laughter synthesis approaches. However, the unconditional generation of the
diversity of human emotional responses in high-quality auditory synthesis
remains an open problem, with important implications for the application of
these approaches in artistic settings. We developed LaughGANter, an approach
to reproduce the diversity of human laughter using generative adversarial
networks (GANs). When trained on a dataset of diverse laughter samples,
LaughGANter generates diverse, high quality laughter samples, and learns a
latent space suitable for emotional analysis and novel artistic applications
such as latent mixing/interpolation and emotional transfer.111Samples:
https://bit.ly/2XQqcW0

##  1 Introduction

Modern society has refined the condition of solitude to the point where
countless seniors, marginalized because of their age, have magically
disappeared: left to their own devices, these individuals fade from social
life and essentially live in a parallel world. The COVID-19 crisis and
resulting lockdowns have both entrenched this phenomenon and helped to reveal
how widespread it really is. Can artificial intelligence (AI) help to
reconnect generations by making them part of a transgenerational art
experience? At the crossroads of laughter—an act of communication between two
individuals—and artificial intelligence—a purely functional entity—can we
rediscover our humanity?

#### An interactive experience.

The end-goal of this project is to connect people via an interactive web
experience driven by synthetic laughter. Using our models, we will explore the
phenomenon of empathy triggered by the sound of laughter, the relationship
between individual memory and laughter, and how the sound of laughter evolves
over a lifetime.

#### Laughter generation for advancing audio synthesis research.

With stunning advancements in image synthesis [1, 2, 3, 4], Generative
Adversarial Networks (GANs) [5] have gained the attention of researchers in
the field of audio synthesis [6, 7, 8, 9]. Synthesizing audio opens new doors
for musicians and artists and enables them to expand their repertoire of
expression [6]. Despite significant progress by the ML community on methods
for audio synthesis, there have been only a few attempts in the topic of
laughter synthesis [10], and none leveraging modern approaches such as GANs.

Compared to speech, laughter is made challenging by its many context-dependent
attributes, such as emotions [11], age, and gender. Moreover, compared to
well-studied topics like speech synthesis, there are not established
evaluation methods for synthesized laughter. Thus laughter synthesis, has the
potential to become a standard benchmark in unconditional audio synthesis.

#### Related work.

Previous work in the field of laughter generation involves [12] the use of
oscillatory system [13], formant synthesis [14], articulatory speech synthesis
[15], and hidden Markov models (HMM) [16]. Recently, some researchers have
also used deep learning [12, 17] methods for laughter synthesis. GANs are
advantageous in learning of a compact latent space allowing for interpolation,
mixing, and style transfer as well as emotional analysis. In this paper, we
propose to use GANs for the purpose of unconditional laughter generation and
manipulation (LaughGANter). Our aim is to enable a unique interactive art
experience that surprises and connects through the primordial intimacy of our
laughter interacting and juxtaposed with others.

##  2 Methodology

We adapt Multi-Scale Gradient GAN (MSG-GAN) [4] for laughter synthesis. Among
other popular image synthesis methods, like DCGAN [18], ProgressiveGAN [1],
and StyleGAN [2], LaughGANter employs multi-scale gradients on a DCGAN
architecture to address the training instability prevalent in GANs.
Progressive growing of network resolutions is avoided to limit the
hyperparameters to be tuned (e.g. training schedule, learning rates for each
resolution, etc) while the multi-scale discriminator penalizes intermediate
and final layer outputs of the generator.

We refer the reader to [4] for an in-depth study of the MSG-GAN architecture.
Concisely, the generator ($G$) samples a random vector $z$ from a normal
distribution and outputs $x=G(z)$. The generated samples are fed into the
discriminator ($D$), along with real samples, in order to measure the
divergence. We perform pixel normalization after every layer in $G$, and
employ the Relativistic Average Hinge loss [19] in $D$. Moreover, inspired by
[20, 21], we explored the impact of induced receptive field expansion, adding
residual blocks with dilations after each upsampling layer in $G$, which
exponentially increases the model’s receptive field and can lead to better
long range correlation in audio data.

#### Categorical Conditional Generation.

A more directed data generation process is employed through a conditional
adaptation of MSG-GAN [22], facilitating the laughter representation learning
given additional context beyond unlabeled laughter (e.g. gender, age, humor
style, etc). Here, categorical information augments the latent noise vector in
$G$, and to each of the multi-scale vectors within $D$, through a
concatenation with an embedding of context information.

(a) Generated Mel spectrogram

(b) Real Mel spectrogram

(c) FID score for LaughGANter

Figure 1: (a)-(b) Log-magnitude spectrograms for real and generated samples
show similar features on qualitative analysis and (c) FID score for
LaughGANter decreases during training, as the diversity of the generated
samples approaches that of the training data distribution.

##  3 Experiments

#### Setup.

Our model is implemented in PyTorch. We use a laughter dataset containing 2145
laughter samples collected by the National Film Board of Canada. Samples are
1-8s long (22.05kHz mono), and were collected (and labeled) from subjects with
different ages and genders (55% male, 45% female; 93% adult, 6% child, 1%
teen). The audio data is augmented using a random combination of additive
noise, shifting, and changing pitch and duration (using pyrubberband). Then,
this data is converted to Mel spectograms and fed into the model. In addition
to qualitative evaluation, i.e., listening to generated samples, we have used
Fréchet inception distance (FID) [23] to assess the diversity of the generated
samples compared to the training dataset. Instead of using Inception features
used in the original FID score, we use features from a classifier (gender and
age group) trained on the spectrograms of our laughter dataset.

## References

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## Supplementary Materials

### Ethical Implications

LaughGANter’s facilitation in the rediscovery of our humanity and re-
connectivity of generations through interactive art experiences allows for
production of a diversity of human emotional responses. Such a system’s
capability in emotional transfer can be used to strengthen the
interrelationship within human-and-machine interaction, however, is in tandem
capable of coaxing accurate and precise emotional responses which could be
used for downstream human manipulation tasks. This project is performed
specifically for artistic purposes, so ethical considerations applied to all
artistic projects also apply to our work.

### Acknowledgements

We would like to thank Arnaud Roussel for his contributions to dataset
processing and early MSG-GAN prototypes, as well as Isabelle Repelin, Isabelle
Limoges, Martin Viau, Stephanie Quevillon, and Marie-Eve Babineau at the
National Film Board of Canada for financial and project management support for
this research.

### Experiments Results

Fig. 2 depicts how the performance of LaughGANter is improved in the course of
training. In particular, Figs. 2(k) and (l) show that LaughGANter can generate
laughter samples that are very close to real samples. Moreover, Fig. 3 depicts
a linear interpolation between the generated laughter of a female and a male.

(a) Epoch 1

(b) Epoch 10

(c) Epoch 100

(d) Epoch 1000

(e) Epoch 2000

(f) Epoch 3000

(g) Epoch 4000

(h) Epoch 5000

(i) Epoch 6000

(j) Epoch 7000

(k) Epoch 8000

(l) A real sample

Figure 2: (a)-(k) Log-magnitude spectrograms of generated laughter samples
during training and (l) a real sample

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

Figure 3: Interpolation between generated laughter respectively identified as
female (a) and male (j)