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Analyzing Audience Responses to Ukrainian Memetic Warfare Tactics

Recent research shows that 52% of gamers tend to stick to just one platform. Breaking it down, mobile game development (iOS and Android) leads with 60% of gamers, while PC (Windows, macOS, Linux) and consoles (PlayStation 4 and 5, Xbox Series X/S, Nintendo Switch) follow closely, capturing 33% and 32% of gamers, respectively. This indicates a strong loyalty among gamers to their preferred platforms, making them a challenging audience to conquer with your game. For game publishers, spreading their games across multiple platforms is the ultimate cheat code to widen their appeal and boost profits. Game developers utilize powerful engines like Unity and Unreal Engine to create games that are playable across multiple platforms. This approach not only boosts user engagement but also increases the number of paid subscriptions and enhances in-game sales, particularly among fans loyal to a single platform. Cross-Platform Custom Game Development: A Path to the Hearts of Over 3 Billion Players Choosing a platform-specific route in custom game development allows creators to fine-tune their games to a system's unique features, like PlayStation 5's superb graphics or iOS's touchscreen. This approach not only highlights the game's strengths on its chosen platform but also streamlines development and may reduce costs. This is ideal for indie teams or smaller studios focused on crafting standout, polished experiences. Embracing a platform-agnostic approach opens doors wider. Making your game accessible on Android, iOS, Xbox, Nintendo Switch, and PCs enhances visibility and connects you with varied gaming communities instantly. With the global gaming population hitting 3.220 billion in 2023 and expected to rise to 3.320 billion in 2024 — 1.48 billion in Asia and 715 million in Europe — going cross-platform taps into this vast audience. This strategy has propelled games like Fortnite and Among Us to global success. Fortnite is available on PS4, PS5, Xbox One, Xbox Series X, Nintendo Switch, and PC, while Among Us connects players on Android, iOS, PC, and Nintendo Switch. These examples highlight the success of reaching players across all devices. Single-Platform Native Game Development Tech Stacks Let's delve into the technology driving today's custom game development. Some companies, such as Stepico, offer the development of both native and cross-platform games, leveraging each platform's strengths to create remarkable gaming experiences. Mobile Game Development Tech In mobile gaming, the magic usually happens with Swift and the Metal API for iOS, producing dazzling graphics, while Android games most often get their shine from Java or Kotlin alongside the Vulkan API. This approach brings titles like Monument Valley on iOS and Doom 3: BFG Edition on Android to life, offering gamers smooth and visually rich experiences right at their fingertips. Console-native Game Development Tools Switching gears to consoles, developers have a different set of tools. With the PlayStation SDK for Sony’s creations and DirectX for the Xbox lineup, games like Spider-Man 2 for PlayStation and Forza Motorsport for Xbox emerge, each offering deep immersion tailored to the console’s hardware. PC Game Development Methods PC, the most versatile platform, opens a world of possibilities with engines like Unity and Unreal Engine, as well as APIs like DirectX or Vulkan. This tech stack enables the creation of expansive worlds in games like Crusader Kings III and Dark Souls Series, providing PC gamers with unparalleled depth and performance. Creating mobile-native games for Android and iOS or tailored PC experiences offers unique benefits. Yet, the true potential is unlocked through cross-platform or multiplatform development. With Unity and Unreal Engine, your game reaches a wider audience, eliminating device boundaries. Broadening Your Reach: Unity vs. Unreal Engine Choosing between Unity and Unreal Engine hinges on your project's needs and team expertise. Both engines offer robust cross-platform support but excel in different areas. Unity is celebrated for its simplicity and versatility, which make it ideal for rapid development across mobile, PC, and console platforms. It's particularly well-suited for custom mobile game development, including 2D games, thanks to its user-friendly interface and extensive asset library. Games like Call of Duty: Mobile and Subnautica illustrate Unity's ability to power both mobile and immersive 3D environments effectively. Unreal Engine is known for its advanced graphics and comprehensive custom game development tools, favored for projects that aim to push the limits of 3D fidelity. Titles like Fortnite and S.T.A.L.K.E.R. 2: Heart of Chornobyl benefit from Unreal's cutting-edge features, such as Nanite and Lumen. These technologies provide exceptional graphical and lighting effects, setting a high bar for gaming experiences. Selecting the optimal game development tools, such as Unity or Unreal Engine, is vital for your game's success. Stepico offers expertise in guiding projects across mobile (Android, iOS), console (PlayStation, Xbox, Nintendo Switch), and PC (Windows, Linux, MacOS). Their services range from ideation to post-launch support, emphasizing proven rapid, quality delivery. By partnering with Stepico, you gain not only strategic insights into tool selection and custom game development approaches but also have professional game developers ready to craft cross-platform setups for you! This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ Recent research shows that 52% of gamers tend to stick to just one platform. Breaking it down, mobile game development (iOS and Android) leads with 60% of gamers, while PC (Windows, macOS, Linux) and consoles (PlayStation 4 and 5, Xbox Series X/S, Nintendo Switch) follow closely, capturing 33% and 32% of gamers, respectively. This indicates a strong loyalty among gamers to their preferred platforms, making them a challenging audience to conquer with your game. For game publishers, spreading their games across multiple platforms is the ultimate cheat code to widen their appeal and boost profits. Game developers utilize powerful engines like Unity and Unreal Engine to create games that are playable across multiple platforms. This approach not only boosts user engagement but also increases the number of paid subscriptions and enhances in-game sales, particularly among fans loyal to a single platform. Cross-Platform Custom Game Development: A Path to the Hearts of Over 3 Billion Players Choosing a platform-specific route in custom game development allows creators to fine-tune their games to a system's unique features, like PlayStation 5's superb graphics or iOS's touchscreen. This approach not only highlights the game's strengths on its chosen platform but also streamlines development and may reduce costs. This is ideal for indie teams or smaller studios focused on crafting standout, polished experiences. Embracing a platform-agnostic approach opens doors wider. Making your game accessible on Android, iOS, Xbox, Nintendo Switch, and PCs enhances visibility and connects you with varied gaming communities instantly. With the global gaming population hitting 3.220 billion in 2023 and expected to rise to 3.320 billion in 2024 — 1.48 billion in Asia and 715 million in Europe — going cross-platform taps into this vast audience. This strategy has propelled games like Fortnite and Among Us to global success. Fortnite is available on PS4, PS5, Xbox One, Xbox Series X, Nintendo Switch, and PC, while Among Us connects players on Android, iOS, PC, and Nintendo Switch. These examples highlight the success of reaching players across all devices. Fortnite Among Us Fortnite Among Us Single-Platform Native Game Development Tech Stacks Let's delve into the technology driving today's custom game development. Some companies, such as Stepico, offer the development of both native and cross-platform games, leveraging each platform's strengths to create remarkable gaming experiences. Stepico , Stepico Mobile Game Development Tech In mobile gaming, the magic usually happens with Swift and the Metal API for iOS, producing dazzling graphics, while Android games most often get their shine from Java or Kotlin alongside the Vulkan API. This approach brings titles like Monument Valley on iOS and Doom 3: BFG Edition on Android to life, offering gamers smooth and visually rich experiences right at their fingertips. Monument Valley Doom 3: BFG Edition Console-native Game Development Tools Switching gears to consoles, developers have a different set of tools. With the PlayStation SDK for Sony’s creations and DirectX for the Xbox lineup, games like Spider-Man 2 for PlayStation and Forza Motorsport for Xbox emerge, each offering deep immersion tailored to the console’s hardware. Spider-Man 2 Forza Motorsport PC Game Development Methods PC, the most versatile platform, opens a world of possibilities with engines like Unity and Unreal Engine, as well as APIs like DirectX or Vulkan. This tech stack enables the creation of expansive worlds in games like Crusader Kings III and Dark Souls Series , providing PC gamers with unparalleled depth and performance. Crusader Kings III Dark Souls Series Creating mobile-native games for Android and iOS or tailored PC experiences offers unique benefits. Yet, the true potential is unlocked through cross-platform or multiplatform development. With Unity and Unreal Engine, your game reaches a wider audience, eliminating device boundaries. Broadening Your Reach: Unity vs. Unreal Engine Choosing between Unity and Unreal Engine hinges on your project's needs and team expertise. Both engines offer robust cross-platform support but excel in different areas. Unity is celebrated for its simplicity and versatility, which make it ideal for rapid development across mobile, PC, and console platforms. It's particularly well-suited for custom mobile game development, including 2D games, thanks to its user-friendly interface and extensive asset library. Games like Call of Duty: Mobile and Subnautica illustrate Unity's ability to power both mobile and immersive 3D environments effectively. Call of Duty: Mobile Subnautica Unreal Engine is known for its advanced graphics and comprehensive custom game development tools, favored for projects that aim to push the limits of 3D fidelity. Titles like Fortnite and S.T.A.L.K.E.R. 2: Heart of Chornobyl benefit from Unreal's cutting-edge features, such as Nanite and Lumen. These technologies provide exceptional graphical and lighting effects, setting a high bar for gaming experiences. Fortnite S.T.A.L.K.E.R. 2: Heart of Chornobyl Selecting the optimal game development tools, such as Unity or Unreal Engine, is vital for your game's success. Stepico offers expertise in guiding projects across mobile (Android, iOS), console (PlayStation, Xbox, Nintendo Switch), and PC (Windows, Linux, MacOS). Their services range from ideation to post-launch support, emphasizing proven rapid, quality delivery. By partnering with Stepico , you gain not only strategic insights into tool selection and custom game development approaches but also have professional game developers ready to craft cross-platform setups for you! partnering with Stepico partnering with Stepico This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ https://business.hackernoon.com/

How Multiplatform Game Development Grows Your Audience: A Guide to Playing Globally

মাল্টিপ্ল্যাটফর্ম গেম ডেভেলপমেন্ট কীভাবে আপনার শ্রোতাদের বৃদ্ধি করে: বিশ্বব্যাপী খেলার জন্য একটি নির্দেশিকা

So vergrößert die Entwicklung plattformübergreifender Spiele Ihr Publikum: Ein Leitfaden zum weltweiten Spielen

Cómo el desarrollo de juegos multiplataforma aumenta su audiencia: una guía para jugar globalmente

Comment le développement de jeux multiplateformes élargit votre audience : un guide pour jouer à l'échelle mondiale

मल्टीप्लेटफॉर्म गेम डेवलपमेंट आपके दर्शकों की संख्या कैसे बढ़ाता है: वैश्विक स्तर पर खेलने के लिए एक गाइड

マルチプラットフォーム ゲーム開発でオーディエンスを拡大する方法: グローバルにプレイするためのガイド

멀티플랫폼 게임 개발로 청중을 늘리는 방법: 글로벌 플레이 가이드

Como o desenvolvimento de jogos multiplataforma aumenta seu público: um guia para jogar globalmente

Как разработка мультиплатформенных игр расширяет вашу аудиторию: руководство по игре в глобальном масштабе

Çok Platformlu Oyun Geliştirme Hedef Kitlenizi Nasıl Büyütür: Dünya Çapında Oynama Rehberi

Cách phát triển trò chơi đa nền tảng giúp tăng lượng khán giả của bạn: Hướng dẫn chơi trên toàn cầu

多平台游戏开发如何扩大受众群体：全球游戏指南

Enabling Acoustic Audience Feedback in Large Virtual Events: Conclusions & References

Authors:
(1) Tamay Aykut, Sureel, Palo Alto, California, USA;
(2) Markus Hofbauer, Technical University of Munich, Germany;
(3) Christopher Kuhn, Technical University of Munich, Germany;
(4) Eckehard Steinbach, Technical University of Munich, Germany;
(5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Related Works Virtual Audience Framework Conclusions & References III. VIRTUAL AUDIENCE FRAMEWORK In this section, we present the proposed virtual audience framework. We focus on online events where a single client provides the content for all other participants in the session. This single presenting client interacts with the audience based on their reactions and uses this as feedback for their future behavior. Such feedback is especially relevant for performing artists that rely on live shows. A. Concept In the scenario we focus on, the presenter depends on the reaction of the entire audience rather than detailed feedback by individuals. In case of multiple participants talking or the entire audience sending feedback at the same time, noise can accumulate and drown out valuable feedback. To avoid accumulating noise, the audio of multiple participants in an online meeting typically needs to be synchronized. Since nonverbal acoustic feedback information such as clapping, whistling, booing, and laughter is less complex than speech, we propose to avoid requiring complex synchronization schemes. We only require an abstract representation of the current audience state denoting which participant is actively clapping, whistling, booing, and laughter. This abstract audience state can then be shared with all participants. The acoustic feedback can then be synthesized locally from the received audience state. This way, each participant can be played the overall acoustic feedback without having to synchronize raw audio. B. Implementation To implement the proposed concept, we share the abstract audience state information with all participants via a central server. We define the audience state as a vector of binary variables for each participant, with each variable representing whether the user is exhibiting a particular reaction. Whenever a client changes their reaction state, the updated state information is sent to the central virtual audience server. The central virtual audience server merges all information and broadcasts the updated current audience state information to every client. Finally, every client synthesises the audience feedback locally for the current audience state. The transmission of such abstract state information results in an additional transmission rate of a few bytes which is fundamentally less compared to the transmission of an audio or video stream. Figure 2 summarizes the concept for the virtual audience framework. On the client side, we define an input interface that collects the abstract state information from the users and an output interface that converts the current audience state information into an audio stream. For synthesizing the audio, any state-ofthe-art technique such as AudioGen [8] or MusicGen [26] can be used. The input interface enables a flexible implementation due to the simplicity of the abstract state information that will be transmitted to the virtual audience server. A Graphical User Interface (GUI) is a simple way for allowing audience contributions. Similar to hand waving in Zoom, participants can share feedback via their GUI. This feedback is collected on the sever and the resulting audience state will be synthesized on every client into an acoustic audience signal. We obtain the abstract state information from the clapping, whistling, booing, and laughter of the user using a GUI or detection methods such as [34]. The transmission of the abstract state information instead of the actual audio signal avoids audio synchronization issues and comes with a negligible transmission overhead as small as a few bytes. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Authors: Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Virtual Audience Framework Virtual Audience Framework Conclusions & References Conclusions & References III. VIRTUAL AUDIENCE FRAMEWORK In this section, we present the proposed virtual audience framework. We focus on online events where a single client provides the content for all other participants in the session. This single presenting client interacts with the audience based on their reactions and uses this as feedback for their future behavior. Such feedback is especially relevant for performing artists that rely on live shows. A. Concept A. Concept In the scenario we focus on, the presenter depends on the reaction of the entire audience rather than detailed feedback by individuals. In case of multiple participants talking or the entire audience sending feedback at the same time, noise can accumulate and drown out valuable feedback. To avoid accumulating noise, the audio of multiple participants in an online meeting typically needs to be synchronized. Since nonverbal acoustic feedback information such as clapping, whistling, booing, and laughter is less complex than speech, we propose to avoid requiring complex synchronization schemes. We only require an abstract representation of the current audience state denoting which participant is actively clapping, whistling, booing, and laughter. This abstract audience state can then be shared with all participants. The acoustic feedback can then be synthesized locally from the received audience state. This way, each participant can be played the overall acoustic feedback without having to synchronize raw audio. B. Implementation B. Implementation To implement the proposed concept, we share the abstract audience state information with all participants via a central server. We define the audience state as a vector of binary variables for each participant, with each variable representing whether the user is exhibiting a particular reaction. Whenever a client changes their reaction state, the updated state information is sent to the central virtual audience server. The central virtual audience server merges all information and broadcasts the updated current audience state information to every client. Finally, every client synthesises the audience feedback locally for the current audience state. The transmission of such abstract state information results in an additional transmission rate of a few bytes which is fundamentally less compared to the transmission of an audio or video stream. Figure 2 summarizes the concept for the virtual audience framework. On the client side, we define an input interface that collects the abstract state information from the users and an output interface that converts the current audience state information into an audio stream. For synthesizing the audio, any state-ofthe-art technique such as AudioGen [8] or MusicGen [26] can be used. The input interface enables a flexible implementation due to the simplicity of the abstract state information that will be transmitted to the virtual audience server. A Graphical User Interface (GUI) is a simple way for allowing audience contributions. Similar to hand waving in Zoom, participants can share feedback via their GUI. This feedback is collected on the sever and the resulting audience state will be synthesized on every client into an acoustic audience signal. We obtain the abstract state information from the clapping, whistling, booing, and laughter of the user using a GUI or detection methods such as [34]. The transmission of the abstract state information instead of the actual audio signal avoids audio synchronization issues and comes with a negligible transmission overhead as small as a few bytes. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Understanding the Virtual Audience Framework

Authors:
(1) Tamay Aykut, Sureel, Palo Alto, California, USA;
(2) Markus Hofbauer, Technical University of Munich, Germany;
(3) Christopher Kuhn, Technical University of Munich, Germany;
(4) Eckehard Steinbach, Technical University of Munich, Germany;
(5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Related Works Virtual Audience Framework Conclusions & References II. RELATED WORK In this section, we summarize techniques for synthesizing acoustic audience feedback. Then, we give an overview of how integrating audience feedback in virtual events is handled currently. A. Sound Synthesis Traditional sound synthesis techniques can be separated into five categories: sample-based, physical modeling, signal modeling,abstract synthesis [9], and learning-based synthesis. More recently, deep-learning-based synthesis approaches have redefined the possibilities in sound synthesis [8]. In sample-based synthesis, audio recordings are cut and spliced together to produce new sounds. The most common example of this is granular synthesis [10]. A sound grain is generally a small element or component of a sound, typically between 10 ms to 200 ms in length. Concatenative synthesis is a subset of granular synthesis [11]. The goal is to select and recombine the grains in a way that avoids perceivable discontinuities. Instead of using prerecorded audio data, physical modeling synthesis aims to model the underlying physical process of a sound system. Physical models require solving partial differential equations for each sample [12]. The resulting models are computationally intensive, requiring significant GPU resources to run in real time [13], [14]. In signal modeling synthesis, sounds are created based on an analysis of real-world sounds. The analyzed waveform is then used for synthesis. The most common method of signal modeling synthesis is Spectral Modeling Synthesis [15]. Spectral modeling can be approached by analyzing the original audio file, selecting a series of sine waves to be used for synthesis, and then combining it with a residual noise shape to produce the original sound [16]. In abstract synthesis, sounds are obtained using abstract methods and algorithms, typically to create entirely new sounds. An example is Frequency Modulation (FM) synthesis [17]. Two sine waves are multiplied together to create a more complex, richer sound that might not exist in the natural world. Early video game sounds were often based on FM synthesis. These sounds can be created and controlled in realtime due to the low complexity of the required process. Finally, in deep-learning-based synthesis, large amounts of recordings are used to obtain a sound synthesis model in a data-driven way [18], [19]. Autoencoders have shown great promise for this task [20], both for music [21] and speech synthesis [22]. Architectures such as WaveNet [3] allow to learn a generative synthesis model directly from real-world recordings, generating significantly more natural sounds than parametric systems. While such models are complex and computationally expensive, recent architectures have increased the inference speed [23], [24], [25]. In 2023, Meta released Audiocraft, which includes text-to-sound systems such as AudioGen [8] or MusicGen [26]. These models allow turning natural text into arbitrary sound, or into music. For the proposed framework, this flexible language-based approach allows easily turning abstract audience feedback data into sound by turning the abstract data into a text prompt first. B. Acoustic Feedback Synthesis Next, we address specific implementations of sound synthesis for creating the most common acoustic feedback sounds of clapping, whistling, booing, and laughter. Since the physical mechanism of Clapping is straightforward, synthesizing clapping sounds can be approached using physical modeling [27]. Whistling can be approached using abstract FM synthesis [28]. Booing can be generated using both abstract or sample-based synthesis [29]. The most complex and challenging sound to synthesize in a virtual audience is Laughter. Since an individual laughter is already a complex sound and additionally varies significantly from person to person, the most promising approaches for laughter synthesis are based on deep learning. Mori et al. [30] used WaveNet [3] to generate synthetic laughter that outperformed laughter synthesized using Hidden Markov Models (HMMs). They used the Online Gaming Voice Chat Corpus [31] to condition WaveNet, allowing them to control the amplitude envelope of the synthesized laughter. Despite the improved naturalness, the resulting laughter was still largely perceived as noisy and echoic. Another approach used transfer learning from models trained with speech data to circumvent the problem of lack of laughter training data [6]. First, text-to-speech is trained and then fine-tuned with smiled speech and laughter data. MelGAN [32] is used to obtain the output waveform. Finally, generative artificial intelligence methods such as Generative Spoken Language Modeling [33] or AudioGen [8] can be used to generate laughter from text prompt. Fine-tuning these models specifically for laughter is a promising direction for natural-sounding laughter synthesis. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Authors: Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Virtual Audience Framework Virtual Audience Framework Conclusions & References Conclusions & References II. RELATED WORK In this section, we summarize techniques for synthesizing acoustic audience feedback. Then, we give an overview of how integrating audience feedback in virtual events is handled currently. A. Sound Synthesis A. Sound Synthesis Traditional sound synthesis techniques can be separated into five categories: sample-based, physical modeling, signal modeling,abstract synthesis [9], and learning-based synthesis. More recently, deep-learning-based synthesis approaches have redefined the possibilities in sound synthesis [8]. In sample-based synthesis, audio recordings are cut and spliced together to produce new sounds. The most common example of this is granular synthesis [10]. A sound grain is generally a small element or component of a sound, typically between 10 ms to 200 ms in length. Concatenative synthesis is a subset of granular synthesis [11]. The goal is to select and recombine the grains in a way that avoids perceivable discontinuities. Instead of using prerecorded audio data, physical modeling synthesis aims to model the underlying physical process of a sound system. Physical models require solving partial differential equations for each sample [12]. The resulting models are computationally intensive, requiring significant GPU resources to run in real time [13], [14]. In signal modeling synthesis, sounds are created based on an analysis of real-world sounds. The analyzed waveform is then used for synthesis. The most common method of signal modeling synthesis is Spectral Modeling Synthesis [15]. Spectral modeling can be approached by analyzing the original audio file, selecting a series of sine waves to be used for synthesis, and then combining it with a residual noise shape to produce the original sound [16]. In abstract synthesis, sounds are obtained using abstract methods and algorithms, typically to create entirely new sounds. An example is Frequency Modulation (FM) synthesis [17]. Two sine waves are multiplied together to create a more complex, richer sound that might not exist in the natural world. Early video game sounds were often based on FM synthesis. These sounds can be created and controlled in realtime due to the low complexity of the required process. Finally, in deep-learning-based synthesis, large amounts of recordings are used to obtain a sound synthesis model in a data-driven way [18], [19]. Autoencoders have shown great promise for this task [20], both for music [21] and speech synthesis [22]. Architectures such as WaveNet [3] allow to learn a generative synthesis model directly from real-world recordings, generating significantly more natural sounds than parametric systems. While such models are complex and computationally expensive, recent architectures have increased the inference speed [23], [24], [25]. In 2023, Meta released Audiocraft, which includes text-to-sound systems such as AudioGen [8] or MusicGen [26]. These models allow turning natural text into arbitrary sound, or into music. For the proposed framework, this flexible language-based approach allows easily turning abstract audience feedback data into sound by turning the abstract data into a text prompt first. B. Acoustic Feedback Synthesis B. Acoustic Feedback Synthesis Next, we address specific implementations of sound synthesis for creating the most common acoustic feedback sounds of clapping, whistling, booing, and laughter. Since the physical mechanism of Clapping is straightforward, synthesizing clapping sounds can be approached using physical modeling [27]. Whistling can be approached using abstract FM synthesis [28]. Booing can be generated using both abstract or sample-based synthesis [29]. The most complex and challenging sound to synthesize in a virtual audience is Laughter. Since an individual laughter is already a complex sound and additionally varies significantly from person to person, the most promising approaches for laughter synthesis are based on deep learning. Mori et al. [30] used WaveNet [3] to generate synthetic laughter that outperformed laughter synthesized using Hidden Markov Models (HMMs). They used the Online Gaming Voice Chat Corpus [31] to condition WaveNet, allowing them to control the amplitude envelope of the synthesized laughter. Despite the improved naturalness, the resulting laughter was still largely perceived as noisy and echoic. Another approach used transfer learning from models trained with speech data to circumvent the problem of lack of laughter training data [6]. First, text-to-speech is trained and then fine-tuned with smiled speech and laughter data. MelGAN [32] is used to obtain the output waveform. Finally, generative artificial intelligence methods such as Generative Spoken Language Modeling [33] or AudioGen [8] can be used to generate laughter from text prompt. Fine-tuning these models specifically for laughter is a promising direction for natural-sounding laughter synthesis. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Related Works on Acoustic Audience Feedback in Large Virtual Events

Authors:
(1) Tamay Aykut, Sureel, Palo Alto, California, USA;
(2) Markus Hofbauer, Technical University of Munich, Germany;
(3) Christopher Kuhn, Technical University of Munich, Germany;
(4) Eckehard Steinbach, Technical University of Munich, Germany;
(5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Related Works Virtual Audience Framework Conclusions & References Abstract The COVID-19 pandemic shifted many events in our daily lives into the virtual domain. While virtual conference systems provide an alternative to physical meetings, larger events require a muted audience to avoid an accumulation of background noise and distorted audio. However, performing artists strongly rely on the feedback of their audience. We propose a concept for a virtual audience framework which supports all participants with the ambience of a real audience. Audience feedback is collected locally, allowing users to express enthusiasm or discontent by selecting means such as clapping, whistling, booing, and laughter. This feedback is sent as abstract information to a virtual audience server. We broadcast the combined virtual audience feedback information to all participants, which can be synthesized as a single acoustic feedback by the client. The synthesis can be done by turning the collective audience feedback into a prompt that is fed to state-of-the-art models such as AudioGen. This way, each user hears a single acoustic feedback sound of the entire virtual event, without requiring to unmute or risk hearing distorted, unsynchronized feedback. Index Terms—Audio Synthesis, Multimedia Conference System, Virtual Audience I. INTRODUCTION Since COVID-19 first hit, many live performances moved to the virtual domain in addition to in-person events. While initially a necessary subpar substitute, virtual events are now a unique new opportunity for performing artists such as comedians, actors, and musicians. However, performing such events without instantaneous feedback is challenging and not comparable with on-site shows. The audience in larger online meeting needs to be muted to avoid disturbing background noise. Interactive audience feedback such as applause or laughter is therefore not possible. So far, virtual conference systems do not offer the functionality to share acoustic audience feedback across the session. Feedback by multiple participants at once is currently restricted to using text chat or signs such as hand waving. This lack of audience interaction poses a significant challenge for performing artists who report that Zoom shows cannot replace in-person performances [1]. It has been shown that integrating laughter into virtual meetings significantly improves the social experience of the participants [2]. Without collective feedback of the audience such as laughter, a core part of human interaction in large events is missing. Integrating acoustic audience feedback into an online conference faces several challenges. Due to network lag, different feedback sounds need to be synchronized and normalized before playing them. Raw audio data from many participants containing feedback such as laughter contains accumulating background noise. An alternative to transmitting raw audio data is to synthesize it. While the field of audio synthesis is mostly focused on speech synthesis [3], [4], [5], nonverbal audio sounds such as laughter can be synthesized as well [6], [7]. More recently, Meta’s Audiocraft [8] further increased the capabilities of audio generation based on abstract information in the form of text prompts. In this paper, we propose to leverage recent advances in audio synthesis of acoustic audience feedback to integrate collective audience sound into virtual events. In contrast to speech, information about the audience feedback such as laughter or clapping can be compressed efficiently into abstract state information. We propose a virtual audience framework that allows audience interaction without the transmission of the actual audio information. The concept of the proposed framework is shown in Figure 1. On the client input side, every participant shares abstract information about their reactions which we merge at a central virtual audience server. We then use the abstract feedback state to synthesize a single joint audience sound, for example by turning the feedback state into a prompt and using text-tosound generative models to obtain the audio. The joint audio sound is then sent back to each user, allowing each user to hear the feedback of the entire audience without overlays or distortion. By continuously updating the feedback state, the audio played for each user can be updated repeatedly to enable an audience sound that fits the current events. The rest of this paper is organized as follows. Section II summarizes related work in the field of audience audio synthesis. We propose a joint acoustic audience framework for large virtual events in Section III. Then, we present a potential implementation of synthesizing joint audio sounds using stateof-the-art generative AI. Section IV concludes the paper. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Authors: Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Virtual Audience Framework Virtual Audience Framework Conclusions & References Conclusions & References Abstract The COVID-19 pandemic shifted many events in our daily lives into the virtual domain. While virtual conference systems provide an alternative to physical meetings, larger events require a muted audience to avoid an accumulation of background noise and distorted audio. However, performing artists strongly rely on the feedback of their audience. We propose a concept for a virtual audience framework which supports all participants with the ambience of a real audience. Audience feedback is collected locally, allowing users to express enthusiasm or discontent by selecting means such as clapping, whistling, booing, and laughter. This feedback is sent as abstract information to a virtual audience server. We broadcast the combined virtual audience feedback information to all participants, which can be synthesized as a single acoustic feedback by the client. The synthesis can be done by turning the collective audience feedback into a prompt that is fed to state-of-the-art models such as AudioGen. This way, each user hears a single acoustic feedback sound of the entire virtual event, without requiring to unmute or risk hearing distorted, unsynchronized feedback. Index Terms —Audio Synthesis, Multimedia Conference System, Virtual Audience Index Terms I. INTRODUCTION Since COVID-19 first hit, many live performances moved to the virtual domain in addition to in-person events. While initially a necessary subpar substitute, virtual events are now a unique new opportunity for performing artists such as comedians, actors, and musicians. However, performing such events without instantaneous feedback is challenging and not comparable with on-site shows. The audience in larger online meeting needs to be muted to avoid disturbing background noise. Interactive audience feedback such as applause or laughter is therefore not possible. So far, virtual conference systems do not offer the functionality to share acoustic audience feedback across the session. Feedback by multiple participants at once is currently restricted to using text chat or signs such as hand waving. This lack of audience interaction poses a significant challenge for performing artists who report that Zoom shows cannot replace in-person performances [1]. It has been shown that integrating laughter into virtual meetings significantly improves the social experience of the participants [2]. Without collective feedback of the audience such as laughter, a core part of human interaction in large events is missing. Integrating acoustic audience feedback into an online conference faces several challenges. Due to network lag, different feedback sounds need to be synchronized and normalized before playing them. Raw audio data from many participants containing feedback such as laughter contains accumulating background noise. An alternative to transmitting raw audio data is to synthesize it. While the field of audio synthesis is mostly focused on speech synthesis [3], [4], [5], nonverbal audio sounds such as laughter can be synthesized as well [6], [7]. More recently, Meta’s Audiocraft [8] further increased the capabilities of audio generation based on abstract information in the form of text prompts. In this paper, we propose to leverage recent advances in audio synthesis of acoustic audience feedback to integrate collective audience sound into virtual events. In contrast to speech, information about the audience feedback such as laughter or clapping can be compressed efficiently into abstract state information. We propose a virtual audience framework that allows audience interaction without the transmission of the actual audio information. The concept of the proposed framework is shown in Figure 1. On the client input side, every participant shares abstract information about their reactions which we merge at a central virtual audience server. We then use the abstract feedback state to synthesize a single joint audience sound, for example by turning the feedback state into a prompt and using text-tosound generative models to obtain the audio. The joint audio sound is then sent back to each user, allowing each user to hear the feedback of the entire audience without overlays or distortion. By continuously updating the feedback state, the audio played for each user can be updated repeatedly to enable an audience sound that fits the current events. The rest of this paper is organized as follows. Section II summarizes related work in the field of audience audio synthesis. We propose a joint acoustic audience framework for large virtual events in Section III. Then, we present a potential implementation of synthesizing joint audio sounds using stateof-the-art generative AI. Section IV concludes the paper. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Enabling Acoustic Audience Feedback in Large Virtual Events

Welcome to another edition of Hack marketing diaries by team HackerNoon! This time, we wanna share our learnings and observations on surveillance capitalism and why we didn't make it our business model. There’s a reason we hate those invasive Ads on Facebook. Who would like their personal and critical information to be used to see those creepily relevant Ads? According to - Meta was also involved in a major about its access to the medical data of patients. The company’s pixel tracking tool was collecting their sensitive information from hospitals including the details about their health, appointments, and medical prescriptions. Pure evil. What's even more annoying? Abrupt Ads stoping you from consuming the content. Classic example - Ads on YouTube. the Markup “Major tax filing services such as H&R Block, TaxAct, and TaxSlayer have been quietly transmitting sensitive financial information to Facebook when Americans file their taxes online.” controversy last year Here’s how HackerNoon saw these issues when we used to become not only a publishing company but also a software company: equity crowdfunding “We don't want to advertise the shoes you've considered buying around the web, we do want to surface sponsors that are relevant to the subject matters being read, such as blockchain sponsors on blockchain content. We believe sponsorship is not evil, if done right. What's evil is surveillance. Our sponsors will be by subject matter. Our promotions are like billboards - relevant to the content and not the individual reader. Our sponsorship placements are minimalistic & non-intrusive. No sidebars, no popups, no video ads. We care about reading quality and will never take that experience away from the reader.” Presenting you HackerNoon’s Targeted Ads by content relevancy - where you target the audience interested in content relevant to your company, without invading their privacy or shoving annoying pop-ups down their throats. Our targeted marketing options allow you to avoid the noise and reach the right audience. It’s a win-win for all. Talking about wins - here's an easter egg for your company - use the code 'HackerFriend' during the meeting and avail 10% off on Targeted Ads! Book a call with us to see the demo Here’s how our targeted Ads by content relevancy work: HackerNoon has curated to date 50,000+ technology tags These tags are grouped into the relevant like , , , , , , and more! parent categories AI Web3 Programming Startups Cybersecurity Finance Every story organically gets at least eight relevant tech tags and a parent category Sponsors buy with all the tags and stories. multimodal placements on relevant categories These Ad placements include Banners, Logos, Newsletter ADs, and Audio ADs - what we call truly AIO (Activities, Interests, and Opinions) Optimized for: Brand Recall and Clickability (Get 3x clicks for the same impressions compared to elsewhere) Get quality leads at unbeatable prices, with CPM ~ $7 and CPC ~ $5. Find pricing and details in the attached deck. to learn how AWS, Bosch, Brave, DataStax, Bright Data, Twilio, SonarSource, Algolia, Algorand, Postman, IONOS, and thousands of other tech companies partner with HackerNoon to target their audiences. Book a meeting with us Until next time!

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The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 32 of 100. HackerNoon’s Legal PDF Series here a. Instagram’s audience composition includes millions of users under the age of 13. 750. Under 16 C.F.R. § 312.2, empirical evidence regarding audience composition is relevant to determining whether an online service, or a portion thereof, is directed to children. 751-754. [Redacted] 755. There are millions of under-13 users on Instagram today. 756. The sheer number of under-13 users of Instagram composing Instagram’s audience demonstrates that Instagram is, in fact, targeted to children. 757. On information and belief, Meta possesses competent and reliable empirical evidence, and such evidence is corroborated by external sources, reflecting the generally known fact that there are millions of under-13 users on Instagram. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

Instagram’s Audience Includes Millions of Users Under the Age of 13

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