A review of differentiable digital signal processing for music and speech synthesis

The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research

Perception of Reverberation in Domestic and Automotive Environments

nrpages: 227status: publishe

Design and Applications of Additive Manufacturing and 3D Printing

Additive manufacturing (AM), more commonly known as 3D printing, has grown trememdously in recent years. It has shown its potential uses in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing, and local on-demand manufacturing are just some of its current benefits. The development of new materials and equipment has opened up new application possibilities, and equipment is quicker and cheaper to use when utilizing the new materials launched by vendors and material developers. AM has become more critical for the industry but also for academics. Since AM offers more design freedom than any other manufacturing process, it provides designers with the challenge of designing better and more efficient products

Effizientes binaurales Rendering von virtuellen akustischen Realitäten : technische und wahrnehmungsbezogene Konzepte

Binaural rendering aims to immerse the listener in a virtual acoustic scene, making it an essential method for spatial audio reproduction in virtual or augmented reality (VR/AR) applications. The growing interest and research in VR/AR solutions yielded many different methods for the binaural rendering of virtual acoustic realities, yet all of them share the fundamental idea that the auditory experience of any sound field can be reproduced by reconstructing its sound pressure at the listener's eardrums. This thesis addresses various state-of-the-art methods for 3 or 6 degrees of freedom (DoF) binaural rendering, technical approaches applied in the context of headphone-based virtual acoustic realities, and recent technical and psychoacoustic research questions in the field of binaural technology. The publications collected in this dissertation focus on technical or perceptual concepts and methods for efficient binaural rendering, which has become increasingly important in research and development due to the rising popularity of mobile consumer VR/AR devices and applications. The thesis is organized into five research topics: Head-Related Transfer Function Processing and Interpolation, Parametric Spatial Audio, Auditory Distance Perception of Nearby Sound Sources, Binaural Rendering of Spherical Microphone Array Data, and Voice Directivity. The results of the studies included in this dissertation extend the current state of research in the respective research topic, answer specific psychoacoustic research questions and thereby yield a better understanding of basic spatial hearing processes, and provide concepts, methods, and design parameters for the future implementation of technically and perceptually efficient binaural rendering.Binaurales Rendering zielt darauf ab, dass der Hörer in eine virtuelle akustische Szene eintaucht, und ist somit eine wesentliche Methode für die räumliche Audiowiedergabe in Anwendungen der virtuellen Realität (VR) oder der erweiterten Realität (AR – aus dem Englischen Augmented Reality). Das wachsende Interesse und die zunehmende Forschung an VR/AR-Lösungen führte zu vielen verschiedenen Methoden für das binaurale Rendering virtueller akustischer Realitäten, die jedoch alle die grundlegende Idee teilen, dass das Hörerlebnis eines beliebigen Schallfeldes durch die Rekonstruktion seines Schalldrucks am Trommelfell des Hörers reproduziert werden kann. Diese Arbeit befasst sich mit verschiedenen modernsten Methoden zur binauralen Wiedergabe mit 3 oder 6 Freiheitsgraden (DoF – aus dem Englischen Degree of Freedom), mit technischen Ansätzen, die im Kontext kopfhörerbasierter virtueller akustischer Realitäten angewandt werden, und mit aktuellen technischen und psychoakustischen Forschungsfragen auf dem Gebiet der Binauraltechnik. Die in dieser Dissertation gesammelten Publikationen befassen sich mit technischen oder wahrnehmungsbezogenen Konzepten und Methoden für effizientes binaurales Rendering, was in der Forschung und Entwicklung aufgrund der zunehmenden Beliebtheit von mobilen Verbraucher-VR/AR-Geräten und -Anwendungen zunehmend an Relevanz gewonnen hat. Die Arbeit ist in fünf Forschungsthemen gegliedert: Verarbeitung und Interpolation von Außenohrübertragungsfunktionen, parametrisches räumliches Audio, auditive Entfernungswahrnehmung ohrnaher Schallquellen, binaurales Rendering von sphärischen Mikrofonarraydaten und Richtcharakteristik der Stimme. Die Ergebnisse der in dieser Dissertation enthaltenen Studien erweitern den aktuellen Forschungsstand im jeweiligen Forschungsfeld, beantworten spezifische psychoakustische Forschungsfragen und führen damit zu einem besseren Verständnis grundlegender räumlicher Hörprozesse, und liefern Konzepte, Methoden und Gestaltungsparameter für die zukünftige Umsetzung eines technisch und wahrnehmungsbezogen effizienten binauralen Renderings.BMBF, 03FH014IX5, Natürliche raumbezogene Darbietung selbsterzeugter Schallereignisse in virtuellen auditiven Umgebungen (NarDasS

Mixed Structural Models for 3D Audio in Virtual Environments

In the world of ICT, strategies for innovation and development are increasingly focusing on applications that require spatial representation and real-time interaction with and within 3D media environments. One of the major challenges that such applications have to address is user-centricity, reflecting e.g. on developing complexity-hiding services so that people can personalize their own delivery of services. In these terms, multimodal interfaces represent a key factor for enabling an inclusive use of the new technology by everyone. In order to achieve this, multimodal realistic models that describe our environment are needed, and in particular models that accurately describe the acoustics of the environment and communication through the auditory modality. Examples of currently active research directions and application areas include 3DTV and future internet, 3D visual-sound scene coding, transmission and reconstruction and teleconferencing systems, to name but a few. The concurrent presence of multimodal senses and activities make multimodal virtual environments potentially flexible and adaptive, allowing users to switch between modalities as needed during the continuously changing conditions of use situation. Augmentation through additional modalities and sensory substitution techniques are compelling ingredients for presenting information non-visually, when the visual bandwidth is overloaded, when data are visually occluded, or when the visual channel is not available to the user (e.g., for visually impaired people). Multimodal systems for the representation of spatial information will largely benefit from the implementation of audio engines that have extensive knowledge of spatial hearing and virtual acoustics. Models for spatial audio can provide accurate dynamic information about the relation between the sound source and the surrounding environment, including the listener and his/her body which acts as an additional filter. Indeed, this information cannot be substituted by any other modality (i.e., visual or tactile). Nevertheless, today's spatial representation of audio within sonification tends to be simplistic and with poor interaction capabilities, being multimedia systems currently focused on graphics processing mostly, and integrated with simple stereo or multi-channel surround-sound. On a much different level lie binaural rendering approaches based on headphone reproduction, taking into account that possible disadvantages (e.g. invasiveness, non-flat frequency responses) are counterbalanced by a number of desirable features. Indeed, these systems might control and/or eliminate reverberation and other acoustic effects of the real listening space, reduce background noise, and provide adaptable and portable audio displays, which are all relevant aspects especially in enhanced contexts. Most of the binaural sound rendering techniques currently exploited in research rely on the use of Head-Related Transfer Functions (HRTFs), i.e. peculiar filters that capture the acoustic effects of the human head and ears. HRTFs allow loyal simulation of the audio signal that arrives at the entrance of the ear canal as a function of the sound source's spatial position. HRTF filters are usually presented under the form of acoustic signals acquired on dummy heads built according to mean anthropometric measurements. Nevertheless, anthropometric features of the human body have a key role in HRTF shaping: several studies have attested how listening to non-individual binaural sounds results in evident localization errors. On the other hand, individual HRTF measurements on a significant number of subjects result both time- and resource-expensive. Several techniques for synthetic HRTF design have been proposed during the last two decades and the most promising one relies on structural HRTF models. In this revolutionary approach, the most important effects involved in spatial sound perception (acoustic delays and shadowing due to head diffraction, reflections on pinna contours and shoulders, resonances inside the ear cavities) are isolated and modeled separately with a corresponding filtering element. HRTF selection and modeling procedures can be determined by physical interpretation: parameters of each rendering blocks or selection criteria can be estimated from real and simulated data and related to anthropometric geometries. Effective personal auditory displays represent an innovative breakthrough for a plethora of applications and structural approach can also allow for effective scalability depending on the available computational resources or bandwidth. Scenes with multiple highly realistic audiovisual objects are easily managed exploiting parallelism of increasingly ubiquitous GPUs (Graphics Processing Units). Building individual headphone equalization with perceptually robust inverse filtering techniques represents a fundamental step towards the creation of personal virtual auditory displays (VADs). To this regard, several examples might benefit from these considerations: multi-channel downmix over headphones, personal cinema, spatial audio rendering in mobile devices, computer-game engines and individual binaural audio standards for movie and music production. This thesis presents a family of approaches that overcome the current limitations of headphone-based 3D audio systems, aiming at building personal auditory displays through structural binaural audio models for an immersive sound reproduction. The resulting models allow for an interesting form of content adaptation and personalization, since they include parameters related to the user's anthropometry in addition to those related to the sound sources and the environment. The covered research directions converge to a novel framework for synthetic HRTF design and customization that combines the structural modeling paradigm with other HRTF selection techniques (inspired by non-individualized HRTF selection procedures) and represents the main novel contribution of this thesis: the Mixed Structural Modeling (MSM) approach considers the global HRTF as a combination of structural components, which can be chosen to be either synthetic or recorded components. In both cases, customization is based on individual anthropometric data, which are used to either fit the model parameters or to select a measured/simulated component within a set of available responses. The definition and experimental validation of the MSM approach addresses several pivotal issues towards the acquisition and delivery of binaural sound scenes and designing guidelines for personalized 3D audio virtual environments holding the potential of novel forms of customized communication and interaction with sound and music content. The thesis also presents a multimodal interactive system which is used to conduct subjective test on multi-sensory integration in virtual environments. Four experimental scenarios are proposed in order to test the capabilities of auditory feedback jointly to tactile or visual modalities. 3D audio feedback related to user’s movements during simple target following tasks is tested as an applicative example of audio-visual rehabilitation system. Perception of direction of footstep sounds interactively generated during walking and provided through headphones highlights how spatial information can clarify the semantic congruence between movement and multimodal feedback. A real time, physically informed audio-tactile interactive system encodes spatial information in the context of virtual map presentation with particular attention to orientation and mobility (O&M) learning processes addressed to visually impaired people. Finally, an experiment analyzes the haptic estimation of size of a virtual 3D object (a stair-step) whereas the exploration is accompanied by a real-time generated auditory feedback whose parameters vary as a function of the height of the interaction point. The collected data from these experiments suggest that well-designed multimodal feedback, exploiting 3D audio models, can definitely be used to improve performance in virtual reality and learning processes in orientation and complex motor tasks, thanks to the high level of attention, engagement, and presence provided to the user. The research framework, based on the MSM approach, serves as an important evaluation tool with the aim of progressively determining the relevant spatial attributes of sound for each application domain. In this perspective, such studies represent a novelty in the current literature on virtual and augmented reality, especially concerning the use of sonification techniques in several aspects of spatial cognition and internal multisensory representation of the body. This thesis is organized as follows. An overview of spatial hearing and binaural technology through headphones is given in Chapter 1. Chapter 2 is devoted to the Mixed Structural Modeling formalism and philosophy. In Chapter 3, topics in structural modeling for each body component are studied, previous research and two new models, i.e. near-field distance dependency and external-ear spectral cue, are presented. Chapter 4 deals with a complete case study of the mixed structural modeling approach and provides insights about the main innovative aspects of such modus operandi. Chapter 5 gives an overview of number of a number of proposed tools for the analysis and synthesis of HRTFs. System architectural guidelines and constraints are discussed in terms of real-time issues, mobility requirements and customized audio delivery. In Chapter 6, two case studies investigate the behavioral importance of spatial attribute of sound and how continuous interaction with virtual environments can benefit from using spatial audio algorithms. Chapter 7 describes a set of experiments aimed at assessing the contribution of binaural audio through headphones in learning processes of spatial cognitive maps and exploration of virtual objects. Finally, conclusions are drawn and new research horizons for further work are exposed in Chapter 8

Automated Rhythmic Transformation of Drum Recordings

Within the creative industries, music information retrieval techniques are now being applied in a variety of music creation and production applications. Audio artists incorporate techniques from music informatics and machine learning (e.g., beat and metre detection) for generative content creation and manipulation systems within the music production setting. Here musicians, desiring a certain sound or aesthetic influenced by the style of artists they admire, may change or replace the rhythmic pattern and sound characteristics (i.e., timbre) of drums in their recordings with those from an idealised recording (e.g., in processes of redrumming and mashup creation). Automated transformation systems for rhythm and timbre can be powerful tools for music producers, allowing them to quickly and easily adjust the different elements of a drum recording to fit the overall style of a song. The aim of this thesis is to develop systems for automated transformation of rhythmic patterns of drum recordings using a subset of techniques from deep learning called deep generative models (DGM) for neural audio synthesis. DGMs such as autoencoders and generative adversarial networks have been shown to be effective for transforming musical signals in a variety of genres as well as for learning the underlying structure of datasets for generation of new audio examples. To this end, modular deep learning-based systems are presented in this thesis with evaluations which measure the extent of the rhythmic modifications generated by different modes of transformation, which include audio style transfer, drum translation and latent space manipulation. The evaluation results underscore both the strengths and constraints of DGMs for transformation of rhythmic patterns as well as neural synthesis of drum sounds within a variety of musical genres. New audio style transfer (AST) functions were specifically designed for mashup-oriented drum recording transformation. The designed loss objectives lowered the computational demands of the AST algorithm and offered rhythmic transformation capabilities which adhere to a larger rhythmic structure of the input to generate music that is both creative and realistic. To extend the transformation possibilities of DGMs, systems based on adversarial autoencoders (AAE) were proposed for drum translation and continuous rhythmic transformation of bar-length patterns. The evaluations which investigated the lower dimensional representations of the latent space of the proposed system based on AAEs with a Gaussian mixture prior (AAE-GM) highlighted the importance of the structure of the disentangled latent distributions of AAE-GM. Furthermore, the proposed system demonstrated improved performance, as evidenced by higher reconstruction metrics, when compared to traditional autoencoder models. This implies that the system can more accurately recreate complex drum sounds, ensuring that the produced rhythmic transformation maintains richness of the source material. For music producers, this means heightened fidelity in drum synthesis and the potential for more expressive and varied drum tracks, enhancing the creativity in music production. This work also enhances neural drum synthesis by introducing a new, diverse dataset of kick, snare, and hi-hat drum samples, along with multiple drum loop datasets for model training and evaluation. Overall, the work in this thesis increased the profile of the field and hopefully will attract more attention and resources to the area, which will help drive future research and development of neural rhythmic transformation systems

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration

Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web

The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability