Sound Design Strategies for Latent Audio Space Explorations Using Deep Learning Architectures

The research in Deep Learning applications in sound and music computing have gathered an interest in the recent years; however, there is still a missing link between these new technologies and on how they can be incorporated into real-world artistic practices. In this work, we explore a well-known Deep Learning architecture called Variational Autoencoders (VAEs). These architectures have been used in many areas for generating latent spaces where data points are organized so that similar data points locate closer to each other. Previously, VAEs have been used for generating latent timbre spaces or latent spaces of symbolic music excepts. Applying VAE to audio features of timbre requires a vocoder to transform the timbre generated by the network to an audio signal, which is computationally expensive. In this work, we apply VAEs to raw audio data directly while bypassing audio feature extraction. This approach allows the practitioners to use any audio recording while giving flexibility and control over the aesthetics through dataset curation. The lower computation time in audio signal generation allows the raw audio approach to be incorporated into real-time applications. In this work, we propose three strategies to explore latent spaces of audio and timbre for sound design applications. By doing so, our aim is to initiate a conversation on artistic approaches and strategies to utilize latent audio spaces in sound and music practices.Comment: In Proceedings of Sound and Music Computing 2023, ISBN 978-91-527-7372-

A Shift In Artistic Practices through Artificial Intelligence

The explosion of content generated by Artificial Intelligence models has initiated a cultural shift in arts, music, and media, where roles are changing, values are shifting, and conventions are challenged. The readily available, vast dataset of the internet has created an environment for AI models to be trained on any content on the web. With AI models shared openly, and used by many, globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring into music, arts, and new media?Comment: Submitted to Leonardo Journa

On the importance of AI research beyond disciplines

As the impact of AI on various scientific fields is increasing, it is crucial to embrace interdisciplinary knowledge to understand the impact of technology on society. The goal is to foster a research environment beyond disciplines that values diversity and creates, critiques and develops new conceptual and theoretical frameworks. Even though research beyond disciplines is essential for understanding complex societal issues and creating positive impact it is notoriously difficult to evaluate and is often not recognized by current academic career progression. The motivation for this paper is to engage in broad discussion across disciplines and identify guiding principles fir AI research beyond disciplines in a structured and inclusive way, revealing new perspectives and contributing to societal and human wellbeing and sustainability

Caring Trouble and Musical AI: Considerations towards a Feminist Musical AI

The ethics of AI as both material and medium for interaction remains in murky waters within the context of musical and artistic practice. The interdisciplinarity of the field is revealing matters of concern and care, which necessitate interdisciplinary methodologies for evaluation to trouble and critique the inheritance of "residue-laden" AI-tools in musical applications. Seeking to unsettle these murky waters, this paper critically examines the example of Holly+, a deep neural network that generates raw audio in the likeness of its creator Holly Herndon. Drawing from theoretical concerns and considerations from speculative feminism and care ethics, we care-fully trouble the structures, frameworks and assumptions that oscillate within and around Holly+. We contribute with several considerations and contemplate future directions for integrating speculative feminism and care into musical-AI agent and system design, derived from our critical feminist examination.Comment: AI and Musical Creativity 202

Comparison Of Micro-Computerized Tomography And Cone-Beam Computerized Tomography In The Detection Of Accessory Canals In Primary Molars

Purpose This study was performed to compare the accuracy of micro-computed tomography (CT) and cone-beam computed tomography (CBCT) in detecting accessory canals in primary molars. Materials and Methods Forty-one extracted human primary first and second molars were embedded in wax blocks and scanned using micro-CT and CBCT. After the images were taken, the samples were processed using a clearing technique and examined under a stereomicroscope in order to establish the gold standard for this study. The specimens were classified into three groups: maxillary molars, mandibular molars with three canals, and mandibular molars with four canals. Differences between the gold standard and the observations made using the imaging methods were calculated using Spearman's rho correlation coefficient test. Results The presence of accessory canals in micro-CT images of maxillary and mandibular root canals showed a statistically significant correlation with the stereomicroscopic images used as a gold standard. No statistically significant correlation was found between the CBCT findings and the stereomicroscopic images. Conclusion Although micro-CT is not suitable for clinical use, it provides more detailed information about minor anatomical structures. However, CBCT is convenient for clinical use but may not be capable of adequately analyzing the internal anatomy of primary teeth.PubMedScopu

Occlusal Caries Depth Measurements Obtained By Five Different Imaging Modalities

The study aimed to assess the accuracy and reproducibility of occlusal caries depth measurements obtained from different imaging modalities. The study comprised 21 human mandibular molar teeth with occlusal caries. Teeth were imaged using film, CCD, two different cone-beam computerized tomography (CBCT) units and a microcomputer tomography (micro-CT). Thereafter, each tooth was serially sectioned, and the section with the deepest carious lesion was scanned using a high-resolution scanner. Each image set was separately viewed by three oral radiologists. Images were viewed randomly, and each set was viewed twice. Lesion depth was measured on film images using a digital caliper, on CCD and CBCT images using built-in measurement tools, on micro-CT images using the Mimics software program, and on histological images using AxioVision Rel. 4.7. Intra- and inter-rater reliabilities were assessed according to the Bland/Altman method by calculating Intraclass Correlation Coefficients (ICCs). Mean/median values obtained with intraoral systems were lower than those obtained with 3-D and histological images for all observers and both readings. Intra-observer ICC values for all observers were highest for histology and micro-CT. In addition, intra-observer ICC values were higher for histology and CBCT than for histology and intra-oral methods. Inter-observer ICC values for first and second readings were high for all observers. No differences in repeatability were found between Accuitomo and Iluma CBCT images or between intra-oral film and CCD images. Micro-CT was found to be the best imaging method for the ex vivo measurement of occlusal caries depth. In addition, both CBCT units performed similarly and better than intra-oral modalities.Wo