S
Samer A. Abdallah
Researcher at University College London
Publications - 58
Citations - 2625
Samer A. Abdallah is an academic researcher from University College London. The author has contributed to research in topics: Independent component analysis & Source separation. The author has an hindex of 21, co-authored 58 publications receiving 2504 citations. Previous affiliations of Samer A. Abdallah include Queen Mary University of London & King's College London.
Papers
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Journal ArticleDOI
A tutorial on onset detection in music signals
TL;DR: Methods based on the use of explicitly predefined signal features: the signal's amplitude envelope, spectral magnitudes and phases, time-frequency representations, and methods based on probabilistic signal models are discussed.
Proceedings Article
The Music Ontology.
TL;DR: The Music Ontology is described: a formal framework for dealing with music-related information on the Semantic Web, including editorial, cultural and acoustic information, and how this ontology can act as a grounding for more domain-specific knowledge representation.
Proceedings Article
Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations.
TL;DR: This paper defines a rigid, contextindependent syntax for representing chord symbols in text, supported with a new database of annotations using this system, and proposes a text represention for musical chord symbols that is simple and intuitive for musically trained individuals to write and understand.
Proceedings Article
Polyphonic music transcription by non-negative sparse coding of power spectra
Samer A. Abdallah,Plumbley +1 more
TL;DR: A novel modification to this model is introduced that recognises that a short-term Fourier spectrum can be thought of as a noisy realisation of the power spectral density of an underlying Gaussian process, where the noise is essentially multiplicative and non-Gaussian.
Book ChapterDOI
Probabilistic Modeling Paradigms for Audio Source Separation
TL;DR: This chapter provides a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models, and discusses promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems.