Categorisation of distortion profiles in relation to audio quality

Since digital audio is encoded as discrete samples of the audio waveform, much can be said about a recording by the statistical properties of these samples. In this paper, a dataset of CD audio samples is analysed; the probability mass function of each audio clip informs a feature set which describes attributes of the musical recording related to loudness, dynamics and distortion. This allows musical recordings to be classified according to their “distortion character”, a concept which describes the nature of amplitude distortion in mastered audio. A subjective test was designed in which such recordings were rated according to the perception of their audio quality. It is shown that participants can discern between three different distortion characters; ratings of audio quality were significantly different (F(1; 2) = 5:72; p < 0:001; eta^2 = 0:008) as were the words used to describe the attributes on which quality was assessed (�Chi^2(8; N = 547) = 33:28; p < 0:001).This expands upon previous work showing links between the effects of dynamic range compression and audio quality in musical recordings, by highlighting perceptual differences

Mapping Acoustic and Semantic Dimensions of Auditory Perception

Auditory categorisation is a function of sensory perception which allows humans to generalise across many different sounds present in the environment and classify them into behaviourally relevant categories. These categories cover not only the variance of acoustic properties of the signal but also a wide variety of sound sources. However, it is unclear to what extent the acoustic structure of sound is associated with, and conveys, different facets of semantic category information. Whether people use such data and what drives their decisions when both acoustic and semantic information about the sound is available, also remains unknown. To answer these questions, we used the existing methods broadly practised in linguistics, acoustics and cognitive science, and bridged these domains by delineating their shared space. Firstly, we took a model-free exploratory approach to examine the underlying structure and inherent patterns in our dataset. To this end, we ran principal components, clustering and multidimensional scaling analyses. At the same time, we drew sound labels’ semantic space topography based on corpus-based word embeddings vectors. We then built an LDA model predicting class membership and compared the model-free approach and model predictions with the actual taxonomy. Finally, by conducting a series of web-based behavioural experiments, we investigated whether acoustic and semantic topographies relate to perceptual judgements. This analysis pipeline showed that natural sound categories could be successfully predicted based on the acoustic information alone and that perception of natural sound categories has some acoustic grounding. Results from our studies help to recognise the role of physical sound characteristics and their meaning in the process of sound perception and give an invaluable insight into the mechanisms governing the machine-based and human classifications

The quality of experience of next generation audio :exploring system, context and human influence factors

PhD ThesisThe next generation of audio reproduction technology has the potential to deliver immersive and personalised experiences to the user; multichannel with-height loudspeaker arrays and binaural techniques offer 3D audio experiences, whereas objectbased techniques offer possibilities of adapting content to suit the system, context and user. A fundamental process in the advancement of such technology is perceptual evaluation. It is crucial to understand how listeners perceive new technology in order to drive future developments. This thesis explores the experience provided by next generation audio technology by taking a quality of experience (QoE) approach to evaluation. System, context and human factors all influence QoE and in this thesis three case studies are presented to explore the role of these categories of influence factors (IFs) in the context of next generation audio evaluation. Furthermore, these case studies explore suitable methods and approaches for the evaluation of the QoE of next generation audio with respect to its various IFs. Specific contributions delivered from these individual studies include a subjective comparison between soundbar and discrete surround sound technology, the application of the Open Profiling of Quality method to the field of audio evaluation, an understanding of both how and why environmental noise influences preferred audio object balance, an understanding of how the influence of technical audio quality on overall listening experience is related to a range of psychographic variables and an assessment of the impact of binaural processing on overall listening experience. When considering these studies as a whole, the research presented here contributes the thesis that to effectively evaluate the perceived quality of next generation audio, a QoE mindset should be taken that considers system, context and human IFs.Engineering and Physical Sciences Research Council (EPSRC) and the British Broadcasting Corporation Research & Development department (BBC R&D

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

The Influence of Training Method on Tone Colour Discrimination

This research addresses the question of whether one of two training methods, identification by continuous adjustment (ICA) or identification by successive approximation (ISA), is more effective in training students using a technical ear training program (TETP). No known empirical studies have examined the effectiveness of either training method within frequency spectrum-based student-targeted TETPs. Preliminary work involved the development of appropriate tests of students’ tone colour discrimination ability in isolation, on tasks sufficiently different from those encountered in TETPs. The tests were then deployed in a pilot study within a pre/post-training scenario using two groups of audio engineering students, one of which undertook an ICA and the other an ISA version of a TETP. These preliminary results indicated the suitability of a test that featured pairwise comparisons of synthetic percussive timbres to show differences in performance between the two training groups. This test was subsequently administered repeatedly in a full-scale study at regular intervals throughout a web-based TETP, in addition to before and after training. Results of the full-scale study showed the individual differences scaling (INDSCAL)-derived stimulus spaces for both groups were similar prior to undertaking the TETP. The ISA group’s post-training results were almost identical to their pre-training results, whereas the ICA groups’ post-training results showed minor, but insignificant differences. Although the full-scale study found insignificant differences in performance between training groups, the preliminary results suggest that the deployment of a pre/post-training test is an effective measure of the training method’s influence on students if the test features a task that is significantly different from those trained on in the TETP

Artificial Intelligence and Online Extremism: Challenges and Opportunities

Radicalisation is a process that historically used to be triggered mainly through social interactions in places of worship, religious schools, prisons, meeting venues, etc. Today, this process is often initiated on the Internet, where radicalisation content is easily shared, and potential candidates are reached more easily, rapidly, and at an unprecedented scale (Edwards and Gribbon, 2013; Von Behr et al., 2013). In recent years, some terrorist organisations succeeded in leveraging the power of social media to recruit individuals to their cause and ideology (Farwell, 2014). It is often the case that such recruitment attempts are initiated on open social media platforms (e.g., Twitter, Facebook, Tumblr, YouTube) but then move onto private messages and/or encrypted platforms (e.g., WhatsApp, Telegram). Such encrypted communication channels have also been used by terrorist cells and networks to plan their operations (Gartenstein-Ross and Barr). To counteract the activities of such organisations, and to halt the spread of radicalisation content, some governments, social media platforms, and counter-extremism agencies are investing in the creation of advanced information technologies to identify and counter extremism through the development of Artificial Intelligent (AI) solutions (Correa and Sureka, 2013; Agarwal and Sureka 2015a; Scrivens and Davies, 2018). These solutions have three main objectives: (i) understanding the phenomena behind online extremism (the communication flow, the use of propaganda, the different stages of the radicalisation process, the variety of radicalisation channels, etc.), (ii) automatically detecting radical users and content, and (iii) predicting the adoption and spreading of extremist ideas. Despite current advancements in the area, multiple challenges still exist, including: (i) the lack of a common definition of prohibited radical and extremist internet activity, (ii) the lack of solid verification of the datasets collected to develop detection and prediction models, (iii) the lack of cooperation across research fields, since most of the developed technological solutions are neither based on, nor do they take advantage of, existing social theories and studies of radicalisation, (iv) the constant evolution of behaviours associated with online extremism in order to avoid being detected by the developed algorithms (changes in terminology, creation of new accounts, etc.) and, (v) the development of ethical guidelines and legislation to regulate the design and development of AI technology to counter radicalisation. In this book chapter we provide an overview of the current technological advancements towards addressing the problem of online extremism (with a particular focus on Jihadism). We identify some of the limitations of current technologies, and highlight some of the potential opportunities. Our aim is to reflect on the current state of the art and to stimulate discussions on the future design and development of AI technology to target the problem of online extremism

Development and applications of a smartphone-based mobile electroencephalography (EEG) system

Electroencephalography (EEG) is a clinical and research technique used to non-invasively acquire brain activity. EEG is performed using static systems in specialist laboratories where participant mobility is constrained. It is desirable to have EEG systems which enable acquisition of brain activity outside such settings. Mobile systems seek to reduce the constraining factors of EEG device and participant mobility to enable recordings in various environments but have had limited success due to various factors including low system specification. The main aim of this thesis was to design, build, test and validate a novel smartphone-based mobile EEG system.A literature review found that the term ‘mobile EEG’ has an ambiguous meaning as researchers have used it to describe many differing degrees of participant and device mobility. A novel categorisation of mobile EEG (CoME) scheme was derived from thirty published EEG studies which defined scores for participant and device mobilities, and system specifications. The CoME scheme was subsequently applied to generate a specification for the proposed mobile EEG system which had 24 channels, sampled at 24 bit at a rate of 250 Hz. Unique aspects of the EEG system were the introduction of a smartphone into the specification, along with the use of Wi-Fi for communications. The smartphone’s processing power was used to remotely control the EEG device so as to enable EEG data capture and storage as well as electrode impedance checking via the app. This was achieved by using the Unity game engine to code an app which provided the flexibility for future development possibilities with its multi-platform support.The prototype smartphone-based waist-mounted mobile EEG system (termed ‘io:bio’) was validated against a commercial FDA clinically approved mobile system (Micromed). The power spectral frequency, amplitude and area of alpha frequency waves were determined in participants with their eyes closed in various postures: lying, sitting, standing and standing with arms raised. Since a correlation analysis to compare two systems has interpretability problems, Bland and Altman plots were utilised with a priori justified limits of agreement to statistically assess the agreement between the two EEG systems. Overall, the results found similar agreements between the io:bio and Micromed systems indicating that the systems could be used interchangeably. Utilising the io:bio and Micromed systems in a walking configuration, led to contamination of EEG channels with artifacts thought to arise from movement and muscle-related sources, and electrode displacement.To enable an event related potential (ERP) capability of the EEG system, additional coding of the smartphone app was undertaken to provide stimulus delivery and associated data marking. Using the waist-mounted io:bio system, an auditory oddball paradigm was also coded into the app, and delivery of auditory tones (standard and deviant) to the participant (sitting posture) achieved via headphones connected to the smartphone. N100, N200 and P300 ERP components were recorded in participants sitting, and larger amplitudes were found for the deviant tones compared to the standard ones. In addition, when the paradigm was tested in individual participants during walking, movement-related artifacts impacted negatively upon the quality of the ERP components, although components were discernible in the grand mean ERP.The io:bio system was redesigned into a head-mounted configuration in an attempt to reduce EEG artifacts during participant walking. The initial approach taken to redesign the system involved using electronic components populated onto a flexible PCB proved to be non-robust. Instead, the rigid PCB form of the circuitry was taken from the io:bio waist-mounted system and placed onto the rear head section of the electrode cap via a bespoke cradle. Using this head-mounted system, in a preliminary auditory oddball paradigm study, ERP responses were obtained in participants whilst walking. Initial results indicate that artifacts are reduced in this head-mounted configuration, and N100, N200 and P300 components are clearly identifiable in some channels

Reimagining the blues: A new narrative for 21st century blues music

This project explores the extent to which blues music in the 21st century is linked to its cultural past through identification and examination of the key concepts and relationships that may contribute to a contemporary understanding of the blues and cultural artefacts, as circulated and consumed in popular music practices. Despite the vast amount of scholarship on blues music, including revisionist literature that emerged in the late 20th century and in the first decade of this century, there has been no singular study of popular music or the blues that has specifically addressed the sociocultural and musicological links between the traditions of the past in the context of 21st century popular music in sufficient depth and so research into contemporary interpretations of blues music as it exists in the 21st century remains relatively scarce. This project provides an account of the cultural resonances and development of the blues genre in popular music culture to establish what the blues means, how it means, and to who it is meaningful through the formulation of a conceptual framework offered as a unique methodological tool for identifying and exploring blues music in the 21st century. Within this interdisciplinary framework, concepts including those concerned with technological mediation, intertextuality, cultural identity, memory, and meaning, are mobilised, refined, and combined in order to reveal and explore problematic relationships that exist in and between concepts of race, place, and technology as connected to blues music in the 21st century. Through an ethnomusicological strategy of enquiry and largely inductive approach to the collection of qualitative and quantitative data, the results of analyses conducted using a broad range of methods including music theoretic analysis, semiotics, intertextuality, survey, and interview are presented in order to both address how and why a contemporary blues music revival may be perceived to be taking place and to offer a fresh historical review of the context in which the blues has developed from a 21st century platform. This study finds that popular music performers and consumers are continually reimagining the blues through engagement with the traditions of the past and accordingly argues for an extension to the boundaries of blues music in its stylistic and cultural categorisation in 21st-century discourse. It is also argued that the results of research presented here also go some way in illustrating both how such engagement with the traditions of the past may directly reflect tensions in contemporary society, and how blues-marketed artefacts are demarcated and declassified within the music industry.N/

Digital Place Narratives and Content Analysis in Tourism Research

The aim of this article is to introduce content analysis in the field of social research on tourism. Among other techniques, content analysis has undergone a revitalisation and acceleration in recent years, especially following a process of digitalisation and the spread of blogs, websites, social media, online reviews and digital spaces in which it is possible to comment, release content and express one’s own opinion, making the information available directly on the web increasingly numerous. On the basis of these premises, the aim of this article is to provide the reader with a definition of the technique by tracing, some specificities and useful characteristics. Subsequently, it will outline the difference between the quantitative and qualitative approaches to content analysis by examining the digital scenario and its particularities, especially the kind of digital data of which it makes use. Finally, it will offer a practical example of its application to the analysis of tourism narratives in digital spaces, centred on Quartieri Spagnoli in Naples. The example will be used in order to show how this technique could be a solution for specific cognitive objectives that are increasingly fitting in the production of tourism studies which adopt digital data in even more articulated designs that may involve a combination of different techniques