Samer Abdallah - Profile on Academia.edu (original) (raw)
Papers by Samer Abdallah
Cognitive Science, 2018
A basic function of cognition is to detect regularities in sensory input to facilitate the predic... more A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners’ expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session.
Analysing Symbolic Music with Probabilistic Grammars
Computational Music Analysis, 2015
When listening to music, we form implicit expectations about the forthcoming temporal sequence. L... more When listening to music, we form implicit expectations about the forthcoming temporal sequence. Listeners acquire knowledge of music through processes such as statistical learning, but how do different types of statistical information affect listeners' learning and memory? To investigate this, we conducted a behavioral study in which participants repeatedly heard tone sequences varying within a range of informationtheoretic measures. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. This enabled us to examine how statistical information affects expectation and memory for tone sequences over a period of increasing exposure. We found significant correlations between listeners' expectedness ratings and measures of information theory (IT), and although listeners demonstrated poor overall memory performance, the IT properties significantly impacted on musical memory. Generally, simple sequences yielded increasingly better memory performance. High-information sequences, for which making accurate predictions is difficult, resulted in consistently poor recognition memory.
An Information-Theoretic Account of Musical Expectation and Memory
Unsupervised analysis of polyphonic music using sparse coding in a probabilistic framework
Method of Analyzing Audio, Music or Video Data
This research focuses on real-time gesture learning and recognition. Events arrive in a continuou... more This research focuses on real-time gesture learning and recognition. Events arrive in a continuous stream without explicitly given boundaries. To obtain temporal accuracy, we need to consider the lag between the detection of an event and any effects we wish to trigger with it. Two methods for real time gesture recognition using a Nintendo Wii controller are presented. The first detects gestures similar to a given template using either a Euclidean distance or a cosine similarity measure. The second method uses novel information theoretic methods to detect and categorize gestures in an unsupervised way. The role of supervision, detection lag and the importance of haptic feedback are discussed.
Sparse Coding of Music Signals
Page 1. Sparse Coding of Music Signals Samer A. Abdallah*and Mark D. Plumbley1, Department of Ele... more Page 1. Sparse Coding of Music Signals Samer A. Abdallah*and Mark D. Plumbley1, Department of Electronic Engineering King's College London March 13, 2001 Abstract We discuss the use of unsupervised learning techniques ...
We used Independent Component Analysis (ICA) with sparse coding to analyze music spectral sequenc... more We used Independent Component Analysis (ICA) with sparse coding to analyze music spectral sequences. We modelled an audio spectrum as an approximate mixture of the spectra of individual notes, using our ICA approach to "unmix" this to find the individual notes and note spectra. Notes are assumed to be approximately independent, and sparse (mostly off). Results on synthesized harpsichord music are encouraging, producing an approximate piano-roll transcription, and a passable rendition of the original music when resynthesized. We are currently working to extend and improve this through the use of temporal information of note activities and to handle more complex timbral behaviour.
We describe a method of visualising geometrically the dependency structure of a distributed repre... more We describe a method of visualising geometrically the dependency structure of a distributed representation. The mutual information between each pair of components is estimated using a nonlinear correlation coefficient, in terms of which a distance measure is defined. Multidimensional scaling is then used to generate a spatial configuration that reproduces these distances, the end result being a spatial representation of the dependency between the components, from which an appropriate topology for the representation may be inferred. The method is applied to ICA representations of speech and music.
Unsupervised Learning for Music Perception
ABSTRACT Perception and cognition can be considered to be processes aimed at discovering the inde... more ABSTRACT Perception and cognition can be considered to be processes aimed at discovering the independent causes behind sensory input. Thus, the goal of perception might be to acheive a factorial coding. Unsupervised learning with neural networks offers a way to implement this using techniques such as sparse coding. This would result in a representation in terms of an optimal set of features, rather than the heuristically guided selection often used now. The Wigner Distribution is a good choice of input to these algorithms for a number of reasons. The principle of factorial coding, applied consistently, could result in natural representations for musical contructs such as melodic phrases or rhythmic motives; something which has obvious applications in music processing. 1 Introduction The problem of music cognition is currently being attacked from a number of directions. One approach grows out of work being done on auditory scene analysis [6, 23], starting with an audio signal and modelling, a...
Exploring probabilistic grammars of symbolic music using PRISM
Comparing models of symbolic music using probabilistic grammars and probabilistic programming
How Predictable Do We Like Our Music? Eliciting Aesthetic Preferences With The Melody Triangle Mobile App
Instantaneous predictive information in Gaussian processes
Model-based audio source separation
... oai:hal.inria.fr:inria-00545140; Contributeur : Emmanuel Vincent <>; Soumis le ... more ... oai:hal.inria.fr:inria-00545140; Contributeur : Emmanuel Vincent <>; Soumis le : Jeudi 9 Décembre 2010, 16:39:33; Dernière modification le : Vendredi 10 Décembre 2010, 16:02:47. Voir la fiche détaillée. Exporter. Bibtex EndNote ...
Information theory and sensory perception
Design and Information in Biology, 2006
Information Dynamics and the Perception of Temporal Structure
Connectionist Models of Behaviour and Cognition Ii, 2009
Probabilistic Modeling Paradigms for Audio Source Separation
Machine Audition, 2010
... MD, & Davies, ME (2011). Probabilistic Modeling Paradigms for Audio Source Separation. In... more ... MD, & Davies, ME (2011). Probabilistic Modeling Paradigms for Audio Source Separation. In Wang, W. (Ed.), Machine Audition: Principles, Algorithms and Systems. (pp. 162-185). doi:10.4018/978-1-61520-919-4.ch007. Chicago. Vincent, Emmanuel, Maria G. Jafari, Samer ...
Cognitive Science, 2018
A basic function of cognition is to detect regularities in sensory input to facilitate the predic... more A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners’ expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session.
Analysing Symbolic Music with Probabilistic Grammars
Computational Music Analysis, 2015
When listening to music, we form implicit expectations about the forthcoming temporal sequence. L... more When listening to music, we form implicit expectations about the forthcoming temporal sequence. Listeners acquire knowledge of music through processes such as statistical learning, but how do different types of statistical information affect listeners' learning and memory? To investigate this, we conducted a behavioral study in which participants repeatedly heard tone sequences varying within a range of informationtheoretic measures. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. This enabled us to examine how statistical information affects expectation and memory for tone sequences over a period of increasing exposure. We found significant correlations between listeners' expectedness ratings and measures of information theory (IT), and although listeners demonstrated poor overall memory performance, the IT properties significantly impacted on musical memory. Generally, simple sequences yielded increasingly better memory performance. High-information sequences, for which making accurate predictions is difficult, resulted in consistently poor recognition memory.
An Information-Theoretic Account of Musical Expectation and Memory
Unsupervised analysis of polyphonic music using sparse coding in a probabilistic framework
Method of Analyzing Audio, Music or Video Data
This research focuses on real-time gesture learning and recognition. Events arrive in a continuou... more This research focuses on real-time gesture learning and recognition. Events arrive in a continuous stream without explicitly given boundaries. To obtain temporal accuracy, we need to consider the lag between the detection of an event and any effects we wish to trigger with it. Two methods for real time gesture recognition using a Nintendo Wii controller are presented. The first detects gestures similar to a given template using either a Euclidean distance or a cosine similarity measure. The second method uses novel information theoretic methods to detect and categorize gestures in an unsupervised way. The role of supervision, detection lag and the importance of haptic feedback are discussed.
Sparse Coding of Music Signals
Page 1. Sparse Coding of Music Signals Samer A. Abdallah*and Mark D. Plumbley1, Department of Ele... more Page 1. Sparse Coding of Music Signals Samer A. Abdallah*and Mark D. Plumbley1, Department of Electronic Engineering King's College London March 13, 2001 Abstract We discuss the use of unsupervised learning techniques ...
We used Independent Component Analysis (ICA) with sparse coding to analyze music spectral sequenc... more We used Independent Component Analysis (ICA) with sparse coding to analyze music spectral sequences. We modelled an audio spectrum as an approximate mixture of the spectra of individual notes, using our ICA approach to "unmix" this to find the individual notes and note spectra. Notes are assumed to be approximately independent, and sparse (mostly off). Results on synthesized harpsichord music are encouraging, producing an approximate piano-roll transcription, and a passable rendition of the original music when resynthesized. We are currently working to extend and improve this through the use of temporal information of note activities and to handle more complex timbral behaviour.
We describe a method of visualising geometrically the dependency structure of a distributed repre... more We describe a method of visualising geometrically the dependency structure of a distributed representation. The mutual information between each pair of components is estimated using a nonlinear correlation coefficient, in terms of which a distance measure is defined. Multidimensional scaling is then used to generate a spatial configuration that reproduces these distances, the end result being a spatial representation of the dependency between the components, from which an appropriate topology for the representation may be inferred. The method is applied to ICA representations of speech and music.
Unsupervised Learning for Music Perception
ABSTRACT Perception and cognition can be considered to be processes aimed at discovering the inde... more ABSTRACT Perception and cognition can be considered to be processes aimed at discovering the independent causes behind sensory input. Thus, the goal of perception might be to acheive a factorial coding. Unsupervised learning with neural networks offers a way to implement this using techniques such as sparse coding. This would result in a representation in terms of an optimal set of features, rather than the heuristically guided selection often used now. The Wigner Distribution is a good choice of input to these algorithms for a number of reasons. The principle of factorial coding, applied consistently, could result in natural representations for musical contructs such as melodic phrases or rhythmic motives; something which has obvious applications in music processing. 1 Introduction The problem of music cognition is currently being attacked from a number of directions. One approach grows out of work being done on auditory scene analysis [6, 23], starting with an audio signal and modelling, a...
Exploring probabilistic grammars of symbolic music using PRISM
Comparing models of symbolic music using probabilistic grammars and probabilistic programming
How Predictable Do We Like Our Music? Eliciting Aesthetic Preferences With The Melody Triangle Mobile App
Instantaneous predictive information in Gaussian processes
Model-based audio source separation
... oai:hal.inria.fr:inria-00545140; Contributeur : Emmanuel Vincent <>; Soumis le ... more ... oai:hal.inria.fr:inria-00545140; Contributeur : Emmanuel Vincent <>; Soumis le : Jeudi 9 Décembre 2010, 16:39:33; Dernière modification le : Vendredi 10 Décembre 2010, 16:02:47. Voir la fiche détaillée. Exporter. Bibtex EndNote ...
Information theory and sensory perception
Design and Information in Biology, 2006
Information Dynamics and the Perception of Temporal Structure
Connectionist Models of Behaviour and Cognition Ii, 2009
Probabilistic Modeling Paradigms for Audio Source Separation
Machine Audition, 2010
... MD, & Davies, ME (2011). Probabilistic Modeling Paradigms for Audio Source Separation. In... more ... MD, & Davies, ME (2011). Probabilistic Modeling Paradigms for Audio Source Separation. In Wang, W. (Ed.), Machine Audition: Principles, Algorithms and Systems. (pp. 162-185). doi:10.4018/978-1-61520-919-4.ch007. Chicago. Vincent, Emmanuel, Maria G. Jafari, Samer ...