42 research outputs found
Fractional delay estimation for blind source separation and localization of temporomandibular joint sounds
Temporomandibular joint (TMJ) sound sources are
generated from the two joints connecting the lower jaw to the
temporal bone. Such sounds are important diagnostic signs in
patients suffering from temporomandibular disorder (TMD).
In this study, we address the problem of source separation of
the TMJ sounds. In particular, we examine patients with only
one TMJ generating âclicks.â Thereafter, we consider the TMJ
sounds recorded from the two auditory canals as mixtures of
clicks from the TMD joint and the noise produced by the other
healthy/normal TMJ.We next exploit the statistical nonstationary
nature of the TMJ signals by employing the degenerate unmixing
estimation technique (DUET) algorithm, a timeâfrequency (TâF)
approach to separate the sources. As the DUET algorithm requires
the sensors to be closely spaced, which is not satisfied by
our recording setup, we have to estimate the delay between the
recorded TMJ sounds to perform an alignment of the mixtures.
Thus, the proposed extension of DUET enables an essentially
arbitrary separation of the sensors. It is also shown that DUET
outperforms the convolutive Infomax algorithm in this particular
TMJ source separation scenario. The spectra of both separated
TMJ sources with our method are comparable to those available
in existing literature. Examination of both spectra suggests that
the click source has a better audible prominence than the healthy
TMJ source. Furthermore, we address the problem of source
localization. This can be achieved automatically by detecting the
sign of our proposed mutual information estimator which exhibits
a maximum at the delay between the two mixtures. As a result,
the localized separated TMJ sources can be of great clinical value
to dental specialists
Blind source separation via independent and sparse component analysis with application to temporomandibular disorder
Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Blind source separation via independent and sparse component analysis with application to temporomandibular disorder
Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Blind source separation via independent and sparse component analysis with application to temporomandibular disorder
Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist
Multimodal methods for blind source separation of audio sources
The enhancement of the performance of frequency domain convolutive
blind source separation (FDCBSS) techniques when applied to the
problem of separating audio sources recorded in a room environment
is the focus of this thesis. This challenging application is termed the
cocktail party problem and the ultimate aim would be to build a machine
which matches the ability of a human being to solve this task.
Human beings exploit both their eyes and their ears in solving this task
and hence they adopt a multimodal approach, i.e. they exploit both
audio and video modalities. New multimodal methods for blind source
separation of audio sources are therefore proposed in this work as a
step towards realizing such a machine.
The geometry of the room environment is initially exploited to improve
the separation performance of a FDCBSS algorithm. The positions
of the human speakers are monitored by video cameras and this
information is incorporated within the FDCBSS algorithm in the form
of constraints added to the underlying cross-power spectral density
matrix-based cost function which measures separation performance. [Continues.
New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method
Localizing the bioelectric phenomena originating from the cerebral cortex
and evoked by auditory and somatosensory stimuli are clear objectives to
both understand how the brain works and to recognize different pathologies.
Diseases such as Parkinsonâs, Alzheimerâs, schizophrenia and epilepsy are intensively
studied to find a cure or accurate diagnosis.
Epilepsy is considered the disease with major prevalence within disorders
with neurological origin. The recurrent and sudden incidence of seizures can
lead to dangerous and possibly life-threatening situations. Since disturbance
of consciousness and sudden loss of motor control often occur without any
warning, the ability to predict epileptic seizures would reduce patientsâ anxiety,
thus considerably improving quality of life and safety.
The common procedure for epilepsy seizure detection is based on brain
activity monitorization via electroencephalogram (EEG) data. This process
consumes a lot of time, especially in the case of long recordings, but the major
problem is the subjective nature of the analysis among specialists when
analyzing the same record. From this perspective, the identification of hidden
dynamical patterns is necessary because they could provide insight into
the underlying physiological mechanisms that occur in the brain.
Time-frequency distributions (TFDs) and adaptive methods have demonstrated
to be good alternatives in designing systems for detecting neurodegenerative
diseases. TFDs are appropriate transformations because they offer
the possibility of analyzing relatively long continuous segments of EEG data
even when the dynamics of the signal are rapidly changing. On the other
hand, most of the detection methods proposed in the literature assume a
clean EEG signal free of artifacts or noise, leaving the preprocessing problem
opened to any denoising algorithm.
In this thesis we have developed two proposals for EEG signal processing:
the first approach consists in electrooculogram (EOG) removal method based
on a combination of ICA and RLS algorithms which automatically cancels
the artifacts produced by eyes movement without the use of external âad
hocâ electrode. This method, called ICA-RLS has been compared with other
techniques that are in the state of the art and has shown to be a good
alternative for artifacts rejection. The second approach is a novel method
in EEG features extraction called tracks extraction (LFE features). This
method is based on the TFDs and partial tracking. Our results in pattern
extractions related to epileptic seizures have shown that tracks extraction is
appropriate in EEG detection and classification tasks, being practical, easily applicable in medical environment and has acceptable computational cost