1,949 research outputs found
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
AXMEDIS 2008
The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage
A study, exploration and development of the interaction of music production techniques in a contemporary desktop setting
As with all computer-based technologies, music production is advancing at a rate comparable to βMooreβs lawβ. Developments within the discipline are gathering momentum exponentially; stretching the boundaries of the field,
deepening the levels to which mediation can be applied, concatenating previously discrete hardware technologies into the desktop domain, demanding greater insight from practitioners to master these technologies and even
defining new genres of music through the increasing potential for sonic creativity to evolve.
This DMus project will draw from the implications of the above developments and study the application of technologies currently available in the desktop environment, from emulations of that which was traditionally hardware to the latest spectrally based audio-manipulation tools. It will investigate the interaction of these technologies, and explore creative
possibilities that were unattainable only a few years ago β all as exemplified through the production of two contrasting albums of music. In addition, new software will be developed to actively contribute to the evolution of music production as we know it. The focus will be on extended production technique and innovation, through both development and context.
The commentary will frame the practical work. It will offer a research context with a number of foci in preference to literal questions, it will qualify the methodology and then form a literature & practice review. It will then present a series of frameworks that analyse music production contexts and technologies in a historical perspective. By setting such a trajectory, the current state-of-the-art can be best placed, and a number of the progressive production techniques associated with the submitted artefacts can then by contextualised. It will terminate with a discussion of the work that moves from the specific to the general
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large
language models and creative image generation systems have demonstrated
impressive capabilities on diverse tasks spanning the arts and humanities.
However, the swift evolution of AGI has also raised critical questions about
its responsible deployment in these culturally significant domains
traditionally seen as profoundly human. This paper provides a comprehensive
analysis of the applications and implications of AGI for text, graphics, audio,
and video pertaining to arts and the humanities. We survey cutting-edge systems
and their usage in areas ranging from poetry to history, marketing to film, and
communication to classical art. We outline substantial concerns pertaining to
factuality, toxicity, biases, and public safety in AGI systems, and propose
mitigation strategies. The paper argues for multi-stakeholder collaboration to
ensure AGI promotes creativity, knowledge, and cultural values without
undermining truth or human dignity. Our timely contribution summarizes a
rapidly developing field, highlighting promising directions while advocating
for responsible progress centering on human flourishing. The analysis lays the
groundwork for further research on aligning AGI's technological capacities with
enduring social goods
Detecting Moments of Stress from Measurements of Wearable Physiological Sensors
There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participantβs environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a βgold standardβ of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participantβs perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans
Cultural Context-Aware Models and IT Applications for the Exploitation of Musical Heritage
Information engineering has always expanded its scope by inspiring innovation in different scientific disciplines. In particular, in the last sixty years, music and engineering have forged a strong connection in the discipline known as βSound and Music Computingβ. Musical heritage is a paradigmatic case that includes several multi-faceted cultural artefacts and traditions. Several issues arise from the analog-digital transfer of cultural objects, concerning their creation, preservation, access, analysis and experiencing. The keystone is the relationship of these digitized cultural objects with their carrier and cultural context. The terms βcultural contextβ and βcultural context awarenessβ are delineated, alongside the concepts of contextual information and metadata. Since they maintain the integrity of the object, its meaning and cultural context, their role is critical. This thesis explores three main case studies concerning historical audio recordings and ancient musical instruments, aiming to delineate models to preserve, analyze, access and experience the digital versions of these three prominent examples of musical heritage.
The first case study concerns analog magnetic tapes, and, in particular, tape music, a particular experimental music born in the second half of the XX century. This case study has relevant implications from the musicology, philology and archivistsβ points of view, since the carrier has a paramount role and the tight connection with its content can easily break during the digitization process or the access phase. With the aim to help musicologists and audio technicians in their work, several tools based on Artificial Intelligence are evaluated in tasks such as the discontinuity detection and equalization recognition. By considering the peculiarities of tape music, the philological problem of stemmatics in digitized audio documents is tackled: an algorithm based on phylogenetic techniques is proposed and assessed, confirming the suitability of these techniques for this task. Then, a methodology for a historically faithful access to digitized tape music recordings is introduced, by considering contextual information and its relationship with the carrier and the replay device. Based on this methodology, an Android app which virtualizes a tape recorder is presented, together with its assessment. Furthermore, two web applications are proposed to faithfully experience digitized 78 rpm discs and magnetic tape recordings, respectively. Finally, a prototype of web application for musicological analysis is presented. This aims to concentrate relevant part of the knowledge acquired in this work into a single interface.
The second case study is a corpus of Arab-Andalusian music, suitable for computational research, which opens new opportunities to musicological studies by applying data-driven analysis. The description of the corpus is based on the five criteria formalized in the CompMusic project of the University Pompeu Fabra of Barcelona: purpose, coverage, completeness, quality and re-usability. Four Jupyter notebooks were developed with the aim to provide a useful tool for computational musicologists for analyzing and using data and metadata of such corpus.
The third case study concerns an exceptional historical musical instrument: an ancient Pan flute exhibited at the Museum of Archaeological Sciences and Art of the University of Padova. The final objective was the creation of a multimedia installation to valorize this precious artifact and to allow visitors to interact with the archaeological find and to learn its history. The case study provided the opportunity to study a methodology suitable for the valorization of this ancient musical instrument, but also extendible to other artifacts or museum collections. Both the methodology and the resulting multimedia installation are presented, followed by the assessment carried out by a multidisciplinary group of experts
The Effect of Artificial Light Pollution on Orientation of Hatchling Loggerhead Sea Turtles (Caretta caretta) in the Grand Strand Region, South Carolina
Sea turtle hatchlings primarily utilize sight to detect differences in elevation and light intensity present along the horizon to navigate from the nest to the waterβs edge. The addition of artificial lights can cause visual misdirection, resulting in disorientation (aimlessly wandering in circular paths) or misorientation (moving in distinct paths away from ocean). Extensive research has been done on effects of high levels of artificial light but little on effects of comparatively lower levels of artificial light on hatchling sea turtle orientation. This study examined these lower intensity areas to identify if there is a threshold of artificial light above which hatchling orientation is negatively affected. During the 2016 nesting season, a Geovision GV-FER5303 non-illuminating infrared camera recorded hatchling trajectories at twenty-one loggerhead sea turtle nests from areas varying in light intensity along the Grand Strand region of South Carolina. Individual and group dynamics for lateral range of movement, orientation deviation, and average speed were measured from each nest to determine if parameters associated with orientation were significantly affected by total and artificial radiance values present at the time of emergence. Lateral range of hatchling movement is not significantly influenced under artificial or total radiance conditions; however, deviation from seaward direction (F(2,299)=43.623, p\u3c0.001; F(3,424)=23.528, p\u3c0.001) and average speed are (F(2,495)=42.612, p\u3c0.001; F(3,648)=14.644, p\u3c0.001). Deviation from brightest light source is significant under total radiance conditions (F(3,427)=11.358, p\u3c0.001) while only marginally significant under artificial radiance conditions (F(2,300)=2.336, p=0.098). Results may help inform current management practices to enhance hatchling survival efforts near northern limit of loggerhead nesting beaches
A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms
A deep understanding of cognitive processes requires functional, quantitative analyses of the steps leading from genetics and the development of nervous system structure to behavior. Molecularly-tractable model systems such as Xenopus laevis and planaria offer an unprecedented opportunity to dissect the mechanisms determining the complex structure of the brain and CNS. A standardized platform that facilitated quantitative analysis of behavior would make a significant impact on evolutionary ethology, neuropharmacology, and cognitive science. While some animal tracking systems exist, the available systems do not allow automated training (feedback to individual subjects in real time, which is necessary for operant conditioning assays). The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations. Here we report the development of a second-generation system that is a highly flexible, powerful machine vision and environmental control platform. In order to enable multidisciplinary studies aimed at understanding the roles of genes in brain function and behavior, and aid other laboratories that do not have the facilities to undergo complex engineering development, we describe the device and the problems that it overcomes. We also present sample data using frog tadpoles and flatworms to illustrate its use. Having solved significant engineering challenges in its construction, the resulting design is a relatively inexpensive instrument of wide relevance for several fields, and will accelerate interdisciplinary discovery in pharmacology, neurobiology, regenerative medicine, and cognitive science
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