Towards an interactive framework for robot dancing applications

Estágio realizado no INESC-Porto e orientado pelo Prof. Doutor Fabien GouyonTese de mestrado integrado. Engenharia Electrotécnica e de Computadores - Major Telecomunicações. Faculdade de Engenharia. Universidade do Porto. 200

Aluminium: Flexible and Light, Towards Sustainable Cities

Aluminium: Flexible and Light is book four in the Towards Sustainable Cities series. It demonstrates the flexibility of aluminium in the many production and fabrication processes that can be used to transform and deploy this light and durable metal, from casting, roll forming, extruding, spinning and direct digital printing. Fabrication processes include: laser and water jet cutting, welding, friction stir welding. The role of aluminium in creating thermally efficient yet highly transparent glazing systems is discussed. Key case studies demonstrating and quantifying the carbon savings arising from the specification of aluminium based architecture include: Kielder Probes by sixteen*(makers), Guy’s Hospital Tower by Penoyre & Prasad, dlr Lexicon by Carr Cotter & Naessens, i360 by Marks Barfield Architects and the Large Hadron Collider at CERN. Included in this book is the first complete history of the use of aluminium in bridge construction from 1933 to the second decade of the twenty-first century

A Novel Framework for Efficient Automated Singer Identification in Large Music Databases

10.1145/1508850.1508856ACM Transactions on Information Systems273ATIS

Electronic musical instruments as interactive exhibits in museums

Whilst recent museum exhibitions have explored electronic musical instruments, the interpretational focus has been on materiality rather than sounds produced. Similarly, whilst authors have ‘followed the instruments’ to find the people who used and designed them, those who create and shape their sounds remain comparatively hidden. To address this problem, this thesis introduces sound genealogy – a methodology towards following the evolution of a sound through material networks and people - as an interpretational framework to support exhibition teams in explicitly connecting sounds to instrument interfaces using multi-sensory interactive exhibits. Adopting this methodology will improve visitors’ experiences of music and sound content, helping them connect sounds from their lived experiences to the instruments associated with them: demonstrating how material networks can influence a sound’s popularity and musical value over time, whilst drawing attention to the people involved in the design and use of both sounds and instruments. Chapter one positions this research within contemporary exhibition practices and analyses the methodologies and literature that define the scope for upcoming discussions. The involvement of the UK’s Science Museum Group institutions is also highlighted. Chapters two to four present three case-study insights based on observations of objects and their sounds, and the use of representative exhibits, in North American, European, and British museums. These case studies were chosen so as to represent a range of instrument categories (synthesizers, samplers, drum machines) and interpretational foci (interface, sound, function). Interview data obtained from exhibition team members highlights the strategies and challenges in co-creating positive exhibit experiences for diverse audiences. Evidence from these case studies also supports the analyses of theories and concepts from museum studies, science and technology studies, and sound studies in chapters five and six. This helps to position - and advocate for - the adoption of a sound genealogy methodology in demonstrating the value of sound through interactivity. Additionally, the anticipation and management of visitor behaviours is considered in the context of successfully attaining learning and entertainment goals. Finally, chapters seven and eight document the creation and evaluation of an original interactive exhibit by the author, supported by the sound genealogy methodology

Scalable audio processing across heterogeneous distributed resources: An investigation into distributed audio processing for Music Information Retrieval

Audio analysis algorithms and frameworks for Music Information Retrieval (MIR) are expanding rapidly, providing new ways to discover non-trivial information from audio sources, beyond that which can be ascertained from unreliable metadata such as ID3 tags. MIR is a broad field and many aspects of the algorithms and analysis components that are used are more accurate given a larger dataset for analysis, and often require extensive computational resources. This thesis investigates if, through the use of modern distributed computing techniques, it is possible to design an MIR system that is scalable as the number of participants increases, which adheres to copyright laws and restrictions, whilst at the same time enabling access to a global database of music for MIR applications and research. A scalable platform for MIR analysis would be of benefit to the MIR and scientific community as a whole. A distributed MIR platform that encompasses the creation of MIR algorithms and workflows, their distribution, results collection and analysis, is presented in this thesis. The framework, called DART - Distributed Audio Retrieval using Triana - is designed to facilitate the submission of MIR algorithms and computational tasks against either remotely held music and audio content, or audio provided and distributed by the MIR researcher. Initially a detailed distributed DART architecture is presented, along with simulations to evaluate the validity and scalability of the architecture. The idea of a parameter sweep experiment to find the optimal parameters of the Sub-Harmonic Summation (SHS) algorithm is presented, in order to test the platform and use it to perform useful and real-world experiments that contribute new knowledge to the field. DART is tested on various pre-existing distributed computing platforms and the feasibility of creating a scalable infrastructure for workflow distribution is investigated throughout the thesis, along with the different workflow distribution platforms that could be integrated into the system. The DART parameter sweep experiments begin on a small scale, working up towards the goal of running experiments on thousands of nodes, in order to truly evaluate the scalability of the DART system. The result of this research is a functional and scalable distributed MIR research platform that is capable of performing real world MIR analysis, as demonstrated by the successful completion of several large scale SHS parameter sweep experiments across a variety of different input data - using various distribution methods - and through finding the optimal parameters of the implemented SHS algorithm. DART is shown to be highly adaptable both in terms of the distributed MIR analysis algorithm, as well as the distributio

Semantic Audio Analysis Utilities and Applications.

PhDExtraction, representation, organisation and application of metadata about audio recordings are in the concern of semantic audio analysis. Our broad interpretation, aligned with recent developments in the field, includes methodological aspects of semantic audio, such as those related to information management, knowledge representation and applications of the extracted information. In particular, we look at how Semantic Web technologies may be used to enhance information management practices in two audio related areas: music informatics and music production. In the first area, we are concerned with music information retrieval (MIR) and related research. We examine how structured data may be used to support reproducibility and provenance of extracted information, and aim to support multi-modality and context adaptation in the analysis. In creative music production, our goals can be summarised as follows: O↵-the-shelf sound editors do not hold appropriately structured information about the edited material, thus human-computer interaction is inefficient. We believe that recent developments in sound analysis and music understanding are capable of bringing about significant improvements in the music production workflow. Providing visual cues related to music structure can serve as an example of intelligent, context-dependent functionality. The central contributions of this work are a Semantic Web ontology for describing recording studios, including a model of technological artefacts used in music production, methodologies for collecting data about music production workflows and describing the work of audio engineers which facilitates capturing their contribution to music production, and finally a framework for creating Web-based applications for automated audio analysis. This has applications demonstrating how Semantic Web technologies and ontologies can facilitate interoperability between music research tools, and the creation of semantic audio software, for instance, for music recommendation, temperament estimation or multi-modal music tutorin

Design and evaluation of dynamic feature-based segmentation on music

viii, 94 leaves : ill. ; 29 cmSegmentation is an indispensable step in the field of Music Information Retrieval (MIR). Segmentation refers to the splitting of a music piece into significant sections. Classically there has been a great deal of attention focused on various issues of segmentation, such as: perceptual segmentation vs. computational segmentation, segmentation evaluations, segmentation algorithms, etc. In this thesis, we conduct a series of perceptual experiments which challenge several of the traditional assumptions with respect to segmentation. Identifying some deficiencies in the current segmentation evaluation methods, we present a novel standardized evaluation approach which considers segmentation as a supportive step towards feature extraction in the MIR process. Furthermore, we propose a simple but effective segmentation algorithm and evaluate it utilizing our evaluation approach

Design, Integration, and Evaluation of IoT-Based Electrochromic Building Envelopes for Visual Comfort and Energy Efficiency

Electrochromic glazing has been identified as the next-generation high-performance glazing material for building envelopes due to its dynamic properties, which allow the buildings to respond to various climate conditions. IoT technologies have improved the sensing, communication, and interactions of building environmental data. Few studies have been done to synthesize the advancements in EC materials and building IoT technologies for better building performance. The challenge remains in the lack of compatible design and simulation tools, limited understanding of integration, and a paucity of evaluation measures to support the convergence between the EC building envelopes and IoT technologies. This research first explores the existing challenges of using EC building envelopes using secondary data analysis and case studies. An IoT-based EC prototype system is developed to demonstrate the feasibility of IoT and EC integration. Functionalities, reliability, interoperability, and scalability are assessed with comparisons of four alternative building envelope systems. Nation-wide evaluations of EC building performance are conducted to show regional differences and trade-offs of visual comfort and energy efficiency. A machine learning approach is proposed to solve the predictive EC control problem under random weather conditions. The best prediction models achieve 91.08% mean accuracy with the 16-climate-zone data set. The importance of predictive variables is also measured in each climate zone to develop a better understanding of the effectiveness of climatic sensors. Additionally, a simulation study is conducted to investigate the relationships between design factors and EC building performance. An instantaneous daylight measure is developed to support active daylight control with IoT-based EC building envelopes