Despite advances in image, video, and motion capture technologies, human interactions are frequently represented as line drawings. Intuitively, drawings provide a useful way of filtering complex, dynamic sequences to produce concise representations of interaction. They also make it possible to represent phenomena such as topic spaces, that do not have a concrete physical manifestation. However, the processes involved in producing these drawings, the advantages and limitations of line drawings as representations, and the implications of drawing as an analytic method have not previously been investigated. This thesis explores the use of drawings to represent human interaction and is informed by the prior experience and abilities of the investigator as a practising visual artist. It begins by discussing the drawing process and how it has been used to capture human activities. Key drawing techniques are identified and tested against an excerpt from an interaction between architects. A series of new drawings are constructed to depict one scene from this interaction, highlighting the contrasts between each drawing technique and their impact on the way shared spaces are represented. A second series of original drawings are produced exploring new ways of representing these spaces, leading to a proposal for a field-based approach that combines gesture paths, fields, and human figures to create a richer analytic representation. A protocol for using this approach to analyse video in practice is developed and evaluated though a sequence of three participatory workshops for researchers in human interaction. The results suggest that the field based process of drawing facilitates the production of spatially enriched graphical representations of qualitative spaces. The thesis concludes that the use of drawing to explore non-metric approaches to shared interactional space, has implications for research in human interaction, interaction design, clinical psychology, anthropology, and discourse analysis, and will find form in new new approaches to contemporary artistic practice.

This thesis is about the design of software which enables groups of people to make music together. Networked musical interaction has been an important aspect of Sound and Music Computing research since the early days, although collaborative music software has yet to gain mainstream popularity, and there is currently limited research on the design of such interfaces. This thesis draws on research from Computer Supported Cooperative Work (CSCW) to explore the design of systems for Collaborative Digital Musical Interaction (CDMI). A central focus of this research is the concept of Awareness: a person’s understanding of what is happening, and of who is doing what. A novel software interface is developed and used over three experimental studies to investigate the effects different interface designs have on the way groups of musicians collaborate. Existing frameworks from CSCW are extended to accommodate the properties of music as an auditory medium, and theories of conventional musical interaction are used to elaborate on the nature of music making as a collaborative and social activity which is focused on process-oriented creativity. This research contributes to the fields of Human-Computer Interaction (HCI), Computer Supported Cooperative Work, and Sound and Music Computing through the identification of empirically derived design implications and recommendations for collaborative musical environments. These guidelines are demonstrated through the design of a hypothetical collaborative music system. This thesis also contributes towards the methodology for evaluating such systems, and considers the distinctions between CDMI and the forms of collaboration traditionally studied within CSCW.

Music Information Retrieval (MIR) tasks, in the main, are concerned with the accurate generation of one of a number of different types of music metadata {beat onsets, or melody extraction, for example. Almost always, they operate on fully mixed digital audio recordings. Commonly, this means that a large amount of signal processing effort is directed towards the isolation, and then identification, of certain highly relevant aspects of the audio mix. In some cases, results of one MIR algorithm are useful, if not essential, to the operation of another { a chord detection algorithm for example, is highly dependent upon accurate pitch detection. Although not clearly defined in all cases, certain rules exist which we may take from music theory in order to assist the task { the particular note intervals which make up a specific chord, for example. On the question of generating accurate, low level music metadata (e.g. chromatic pitch and score onset time), a potentially huge advantage lies in the use of multitrack, rather than mixed, audio recordings, in which the separate instrument recordings may be analysed in isolation. Additionally, in MIR, as in many other research areas currently, there is an increasing push towards the use of the Semantic Web for publishing metadata using the Resource Description Framework (RDF). Semantic Web technologies, though, also facilitate the querying of data via the SPARQL query language, as well as logical inferencing via the careful creation and use of web ontology language (OWL) ontologies. This, in turn, opens up the intriguing possibility of deferring our decision regarding which particular type of MIR query to ask of our low-level music metadata until some point later down the line, long after all the heavy signal processing has been carried out. In this thesis, we describe an over-arching vision for an alternative MIR paradigm, built around the principles of early, studio-based metadata capture, and exploitation of open, machine-readable Semantic Web data. Using the specific example of structural segmentation, we demonstrate that by analysing multitrack rather than mixed audio, we are able to achieve a significant and quantifiable increase in the accuracy of our segmentation algorithm. We also provide details of a new multitrack audio dataset with structural segmentation annotations, created as part of this research, and available for public use. Furthermore, we show that it is possible to fully implement a pair of pattern discovery algorithms (the SIA and SIATEC algorithms { highly applicable, but not restricted to, symbolic music data analysis) using only SemanticWeb technologies { the SPARQL query language, acting on RDF data, in tandem with a small OWL ontology. We describe the challenges encountered by taking this approach, the particular solution we've arrived at, and we evaluate the implementation both in terms of its execution time, and also within the wider context of our vision for a new MIR paradigm.

The effect of technology on music has been indisputably profound. As a cultural descendant of the traditions first established by notation and printing, recorded music technology has transformed our understanding and use of music in all sorts of ways. Whether or not different technologies have had a positive or negative effect, however, is a subject of much debate. Traditional histories of recorded music technology demonstrate a tendency either to treat each new platform as truly revolutionary, or to elide the differences between them to such an extent that significant socio-­cultural and socio-­economic transformations become occluded. Revisiting this history with an open mind—and a degree of cultural and temporal distance—permits a perspective of progression, from which the ramifications of recorded music technologies become more accurately discernible. This thesis highlights six characteristics of recorded music, all of which have been affected at various times and in various ways by the evolution of recording technology. Without exception, every new platform for recorded music has improved upon at least one of these six characteristics, although not necessarily without detriment to one of the others. It is in the scope of these improvements that digital music files, as a platform, are fundamentally different to any of their predecessors. Far from simply continuing or exaggerating the trend that forms the customary narrative of the traditional histories of recorded music, digital music files have completely reengineered our relationship with and understanding of the production, distribution, and consumption of music in a very profound way. The thesis explores these changes, and offers some frank yet ultimately encouraging insights as to what the social future of music may hold.

Wireless Sensor Network (WSN) technology has been one of the major avenues of Internet of Things (IoT) due to their potential role in digitising smart physical environments. WSNs are typically composed of a vast number of low-power, low–cost and multifunctional sensor nodes within an area that automatically cooperate to complete the application task. This emerging technology has already contributed to the advancement of a broad range of applications. Nevertheless, the development of WSNs is a challenging issue due to significant concerns, which need to be resolved to take full benefit of this remarkable technology. One of the main challenges of WSNs is how to reduce the energy consumption of a single node, in order to extend the network lifetime and improves the quality of service. For that reason, a newly design energy efficient communication protocol is required to tackle the issue. The clustering protocols designed for communication are alleged to be one of the most efficient solutions that can contribute to network scalability and energy consumption in WSNs. While different clustering protocols have been proposed to tackle the aforementioned issue, those solutions are either not scalable or do not provide the mechanisms to avoid a heavy loaded area. This thesis presents new adaptive energy management mechanisms, through which the limited critical energy source can be wisely managed so that the WSN application can achieve its intended design goals. Three protocols are introduced to manage the energy use. The first protocol presents an intra-cluster CH rotation approach that reduces the need for the execution of a periodical clustering process. The second protocol relates to load balancing in terms of the intra and inter-cluster communication patterns of clusters of unequal sizes. This proposed approach involves computing a threshold value that, when reached, triggers overall network re-clustering, with the condition that the network will be reconfigured into unequal cluster size. The third protocol proposes new performance factors in relation to CH selection. Based on these factors, the aggregated weight of each node is calculated, and the most suitable CH is selected. A comparison with existing communication protocols reveals that the proposed approaches balance effectively the energy consumption among all sensor nodes and significantly increase the network lifetime.

Le transcodage est un élément clé dans la transmission vidéo permettant à une séquence vidéo de passer d'un type de codage à un autre afin de s'adapter au mieux aux capacités de transport d'un canal de transmission. L'intérêt de ce type de traitement est de faire profiter un maximum d'utilisateurs possédant des terminaux variés dont la résolution spatiale, la résolution temporelle affichable, et le type de canal utilisé pour accéder au média varient fortement, et cela à partir d'une seule source de qualité et résolution maximale, stockée sur un serveur, par exemple. Le transcodage est adapté dans les cas où l'on souhaite envoyer une séquence vidéo vers un destinataire et dont le chemin serait constitué de divers canaux de transmission. Nous avons réalisé un transcodeur par requantification ainsi qu'un transcodeur par troncature. Ces deux méthodes ont été comparées et il apparait qu'en termes de qualité d'image l'une ou l'autre de ces méthodes est plus efficace selon le contexte. La suite de nos travaux consiste en l'étude du standard scalable dérivé de H.264 AVC, le standard SVC (Scalable Video Coding). Nous avons souhaité étudier un transcodeur en qualité, mais aussi en résolution spatiale qui permettra de réécrire le flux SVC en un flux AVC décodable par les décodeurs du marché actuel. Cette transposition est réalisée grâce à une architecture reconfigurable permettant de s'adapter aux nombreux types de flux pouvant être conformes au standard SVC d' H.264. L'étude proposée a aboutie à une implémentation partielle d'un transcodeur du type SVC vers AVC. Nous proposons dans cette thèse une description des implémentations de transcodage concernant les formats AVC puis SVC / Transcoding is a key element in the video transmission allows a video to go from one encoding type to another in order to adapt better to the transport capacity of a transmission channel. The advantage of this type of treatment is to make the most of users with various terminals with spatial resolution, temporal resolution displayable, and type of channel used to access the media vary widely, and from that of a single source of quality and maximum resolution, stored on a server, for example. Transcoding is appropriate where you want to send a video to a recipient and whose path would consist of various transmission channels. We realized by a transcoder and a requantization transcoder by truncation. These two methods were compared and it appears that in terms of image quality in either of these methods is more effective depending on the context. Following our work is the study of the standard H.264 AVC scalable derivative of the standard SVC (Scalable Video Coding). We wanted to study as a transcoder, but also in spatial resolution which will rewrite the SVC flow in a stream stroke decodable by decoders on the market today. This mapping is achieved through are configurable architecture to adapt to many types of flow which may conform to standard SVC to H.264. The proposed study has accomplished a partial implementation of a transcoder type SVC to AVC. We propose here a description of the implementations on AVC transcoding and SVC

Ontologies provide an explicit conceptualisation of a domain and a uniform framework that represents domain knowledge in a machine interpretable format. The Semantic Web heavily relies on ontologies to provide well-defined meaning and support for automated services based on the description of semantics. However, considering the open, evolving and decentralised nature of the SemanticWeb – though many ontology engineering tools have been developed over the last decade – it can be a laborious and challenging task to deal with manual annotation, hierarchical structuring and organisation of data as well as maintenance of previously designed ontology structures. For these reasons, we investigate how to facilitate the process of ontology construction using semantic audio analysis. The work presented in this thesis contributes to solving the problems of knowledge acquisition and manual construction of ontologies. We develop a hybrid system that involves a formal method of automatic ontology generation for web-based audio signal processing applications. The proposed system uses timbre features extracted from audio recordings of various musical instruments. The proposed system is evaluated using a database of isolated notes and melodic phrases recorded in neutral conditions, and we make a detailed comparison between musical instrument recognition models to investigate their effects on the automatic ontology generation system. Finally, the automatically-generated musical instrument ontologies are evaluated in comparison with the terminology and hierarchical structure of the Hornbostel and Sachs organology system. We show that the proposed system is applicable in multi-disciplinary fields that deal with knowledge management and knowledge representation issues.

Reconstructing the 3D geometry of scenes based on monocular image sequences is a long-standing problem in computer vision. Structure from motion (SfM) aims at a data-driven approach without requiring a priori models of the scene. When the scene is rigid, SfM is a well understood problem with solutions widely used in industry. However, if the scene is non-rigid, monocular reconstruction without additional information is an ill-posed problem and no satisfactory solution has yet been found. Current non-rigid SfM (NRSfM) methods typically aim at modelling deformable motion globally. Additionally, most of these methods focus on cases where deformable motion is seen as small variations from a mean shape. In turn, these methods fail at reconstructing highly deformable objects such as a flag waving in the wind. Additionally, reconstructions typically consist of low detail, sparse point-cloud representation of objects. In this thesis we aim at reconstructing highly deformable surfaces by modelling them locally. In line with a recent trend in NRSfM, we propose a piecewise approach which reconstructs local overlapping regions independently. These reconstructions are merged into a global object by imposing 3D consistency of the overlapping regions. We propose our own local model – the Quadratic Deformation model – and show how patch division and reconstruction can be formulated in a principled approach by alternating at minimizing a single geometric cost – the image re-projection error of the reconstruction. Moreover, we extend our approach to dense NRSfM, where reconstructions are preformed at the pixel level, improving the detail of state of the art reconstructions. Finally we show how our principled approach can be used to perform simultaneous segmentation and reconstruction of articulated motion, recovering meaningful segments which provide a coarse 3D skeleton of the object.

Αυτή η διπλωματική εργασία έχει ως αντικείμενο την ανάλυση στερεοφωνικού σήματος με παραμέτρους αντίληψης χώρου. Η παραπάνω ανάλυση υλοποιήθηκε με πέντε διαφορετικές μεθόδους υλοποίησης. Στόχος είναι η αναπαραγωγή πολυκαναλικού ήχου τριών καναλιών από το προϋπάρχον στερεοφωνικό υλικό. Στα πλαίσια της εργασίας, διεξήχθη ακουστικό πείραμα για την αξιολόγηση της παραμόρφωσης που εισάγεται από κάθε μέθοδο. / Spatial decomposition stereophonic sound.

Digital technology and the Internet have changed the music industry's landscape. Music has become more accessible allowing consumers to store and share thousands of items in their computer's hard disk, portable media player, mobile phoone and other devices. Recent developments allow consumers to store digital music on the Internet through cloud storage. Given the large music collections available, there is a need for new applications for browsing, organising, discovering and generating playlists for users. In previous years, searching for music has been similar to a textual information search. However, this limits music discovery as it usually requires specific information that may be unknown to the user. This thesis investigates three of the core components of content-based music retrieval: audio features, similarity functions and indexing methods. In the content-based paradigm, audio files are analyzed using their waveform and are represented by high-dimensional features. This study focuses on polyphonic timbre similarity. Polyphonic timbre is the characteristic that allows listeners to differentiate between two music signals or complex instrumental textures with the same perceived pitch and loudness. The different attributes of timbre are examined and suitable features that can be used for music retrieval using timbre similarity are investigated. Evaluations are performed to compare the performance of these features. To improve the overall performance and reduce the undesirable effects of operating in high-dimensionality space, methods on how feature spaces can be combined are also explored. A full linear scan of the feature space is impractical for large music collections. Hence, the filter-and-refine method is adopted to expedite the retrieval process. The objective is to filter a dataset by quickly returning a set of candidate songs then refining the results using an exact similarity measure. Some novel modifications of the filtering step are made to ensure that the level of performance is maintained. The application of our timbre similarity systems are extended to automatic audio classification. In the paradigm, anunlabeled track is tagged with the label of the nearest track. Finally, the performance of our similarity estimator and audio classifier are validated in the annual Music Information Retrieval Evaluation eXchange (MIREX). The MIREX results show that our techniques are state-of-the-art methods.