100 research outputs found
Music genre classification via joint sparse low-rank representation of audio features
A novel framework for music genre classification, namely the joint sparse low-rank representation (JSLRR) is proposed in order to: 1) smooth the noise in the test samples, and 2) identify the subspaces that the test samples lie onto. An efficient algorithm is proposed for obtaining the JSLRR and a novel classifier is developed, which is referred to as the JSLRR-based classifier. Special cases of the JSLRR-based classifier are the joint sparse representation-based classifier and the low-rank representation-based one. The performance of the three aforementioned classifiers is compared against that of the sparse representation-based classifier, the nearest subspace classifier, the support vector machines, and the nearest neighbor classifier for music genre classification on six manually annotated benchmark datasets. The best classification results reported here are comparable with or slightly superior than those obtained by the state-of-the-art music genre classification methods
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A computational study on outliers in world music
The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as âoutliersâ. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the âuniquenessâ of the music of each country
Statistical pattern recognition for audio-forensics : empirical investigations on the application scenarios audio steganalysis and microphone forensics
Magdeburg, Univ., Fak. fĂŒr Informatik, Diss., 2013von Christian KrĂ€tze
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Non classical concept representation and reasoning in formal ontologies
Formal ontologies are nowadays widely considered a standard tool for knowledge
representation and reasoning in the Semantic Web. In this context, they are expected to
play an important role in helping automated processes to access information. Namely:
they are expected to provide a formal structure able to explicate the relationships
between different concepts/terms, thus allowing intelligent agents to interpret, correctly,
the semantics of the web resources improving the performances of the search
technologies.
Here we take into account a problem regarding Knowledge Representation in general,
and ontology based representations in particular; namely: the fact that knowledge
modeling seems to be constrained between conflicting requirements, such as
compositionality, on the one hand and the need to represent prototypical information on
the other. In particular, most common sense concepts seem not to be captured by the
stringent semantics expressed by such formalisms as, for example, Description Logics
(which are the formalisms on which the ontology languages have been built). The aim
of this work is to analyse this problem, suggesting a possible solution suitable for
formal ontologies and semantic web representations.
The questions guiding this research, in fact, have been: is it possible to provide a formal
representational framework which, for the same concept, combines both the classical
modelling view (accounting for compositional information) and defeasible, prototypical
knowledge ? Is it possible to propose a modelling architecture able to provide different
type of reasoning (e.g. classical deductive reasoning for the compositional component
and a non monotonic reasoning for the prototypical one)?
We suggest a possible answer to these questions proposing a modelling framework able
to represent, within the semantic web languages, a multilevel representation of
conceptual information, integrating both classical and non classical (typicality based)
information. Within this framework we hypothesise, at least in principle, the coexistence of multiple reasoning processes involving the different levels of
representation
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective
The doctoral research abstract. Vol:9 2016 / Institute of Graduate Studies, UiTM
FOREWORD:
Seventy three doctoral graduands will be receiving their scroll today signifying their
achievements in completing their PhD journey. The novelty of their research is shared with
you through The Doctoral Abstracts on this auspicious occasion, UiTM 84th Convocation.
We are indeed proud that another 73 scholarly contributions to the world of knowledge
and innovation have taken place through their doctoral research ranging from Science and
Technology, Business and Administration, and Social Science and Humanities.
As we rejoice and celebrate your achievement, we would like to acknowledge
dearly departed Dr Halimi Zakariaâs scholarly contribution entitled
âImpact of Antecedent Factors on Collaborative Technologies Usage
among Academic Researchers in Malaysian Research Universitiesâ. He
has left behind his discovery to be used by other researchers in their quest
of pursuing research in the same area, a discovery that his family can be
proud of.
Graduands, earning your PhD is not the end of discovering new ideas,
invention or innovation but rather the start of discovering something
new. Enjoy every moment of its discovery and embrace that life is
full of mystery and treasure that is waiting for you to unfold. As
you unfold lifeâs mystery, remember you have a friend to count
on, and that friend is UiTM.
Congratulations for completing this academic journey. Keep
UiTM close to your heart and be our ambassador wherever
you go. / Prof Emeritus Datoâ Dr Hassan Said
Vice Chancellor
Universiti Teknologi MAR
Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology
Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology
Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
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