11 research outputs found
Blind Restoration of Real-World Audio by 1D Operational GANs
Objective: Despite numerous studies proposed for audio restoration in the
literature, most of them focus on an isolated restoration problem such as
denoising or dereverberation, ignoring other artifacts. Moreover, assuming a
noisy or reverberant environment with limited number of fixed
signal-to-distortion ratio (SDR) levels is a common practice. However,
real-world audio is often corrupted by a blend of artifacts such as
reverberation, sensor noise, and background audio mixture with varying types,
severities, and duration. In this study, we propose a novel approach for blind
restoration of real-world audio signals by Operational Generative Adversarial
Networks (Op-GANs) with temporal and spectral objective metrics to enhance the
quality of restored audio signal regardless of the type and severity of each
artifact corrupting it. Methods: 1D Operational-GANs are used with generative
neuron model optimized for blind restoration of any corrupted audio signal.
Results: The proposed approach has been evaluated extensively over the
benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with
a random blend of artifacts each with a random severity to mimic real-world
audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved,
respectively, which are substantial when compared with the baseline methods.
Significance: This is a pioneer study in blind audio restoration with the
unique capability of direct (time-domain) restoration of real-world audio
whilst achieving an unprecedented level of performance for a wide SDR range and
artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally
effective real-world audio restoration with significantly improved performance.
The source codes and the generated real-world audio datasets are shared
publicly with the research community in a dedicated GitHub repository1
Zero-Shot Motor Health Monitoring by Blind Domain Transition
Continuous long-term monitoring of motor health is crucial for the early
detection of abnormalities such as bearing faults (up to 51% of motor failures
are attributed to bearing faults). Despite numerous methodologies proposed for
bearing fault detection, most of them require normal (healthy) and abnormal
(faulty) data for training. Even with the recent deep learning (DL)
methodologies trained on the labeled data from the same machine, the
classification accuracy significantly deteriorates when one or few conditions
are altered. Furthermore, their performance suffers significantly or may
entirely fail when they are tested on another machine with entirely different
healthy and faulty signal patterns. To address this need, in this pilot study,
we propose a zero-shot bearing fault detection method that can detect any fault
on a new (target) machine regardless of the working conditions, sensor
parameters, or fault characteristics. To accomplish this objective, a 1D
Operational Generative Adversarial Network (Op-GAN) first characterizes the
transition between normal and fault vibration signals of (a) source machine(s)
under various conditions, sensor parameters, and fault types. Then for a target
machine, the potential faulty signals can be generated, and over its actual
healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN
fault detector can then be trained to detect the real faulty condition in real
time whenever it occurs. To validate the proposed approach, a new benchmark
dataset is created using two different motors working under different
conditions and sensor locations. Experimental results demonstrate that this
novel approach can accurately detect any bearing fault achieving an average
recall rate of around 89% and 95% on two target machines regardless of its
type, severity, and location.Comment: 13 pages, 9 figures, Journa
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
The recently proposed network model, Operational Neural Networks (ONNs), can
generalize the conventional Convolutional Neural Networks (CNNs) that are
homogenous only with a linear neuron model. As a heterogenous network model,
ONNs are based on a generalized neuron model that can encapsulate any set of
non-linear operators to boost diversity and to learn highly complex and
multi-modal functions or spaces with minimal network complexity and training
data. However, the default search method to find optimal operators in ONNs, the
so-called Greedy Iterative Search (GIS) method, usually takes several training
sessions to find a single operator set per layer. This is not only
computationally demanding, also the network heterogeneity is limited since the
same set of operators will then be used for all neurons in each layer. To
address this deficiency and exploit a superior level of heterogeneity, in this
study the focus is drawn on searching the best-possible operator set(s) for the
hidden neurons of the network based on the Synaptic Plasticity paradigm that
poses the essential learning theory in biological neurons. During training,
each operator set in the library can be evaluated by their synaptic plasticity
level, ranked from the worst to the best, and an elite ONN can then be
configured using the top ranked operator sets found at each hidden layer.
Experimental results over highly challenging problems demonstrate that the
elite ONNs even with few neurons and layers can achieve a superior learning
performance than GIS-based ONNs and as a result the performance gap over the
CNNs further widens.Comment: 15 pages, 19 figures, journal manuscrip
Self-Organized Operational Neural Networks with Generative Neurons
Operational Neural Networks (ONNs) have recently been proposed to address the
well-known limitations and drawbacks of conventional Convolutional Neural
Networks (CNNs) such as network homogeneity with the sole linear neuron model.
ONNs are heterogenous networks with a generalized neuron model that can
encapsulate any set of non-linear operators to boost diversity and to learn
highly complex and multi-modal functions or spaces with minimal network
complexity and training data. However, Greedy Iterative Search (GIS) method,
which is the search method used to find optimal operators in ONNs takes many
training sessions to find a single operator set per layer. This is not only
computationally demanding, but the network heterogeneity is also limited since
the same set of operators will then be used for all neurons in each layer.
Moreover, the performance of ONNs directly depends on the operator set library
used, which introduces a certain risk of performance degradation especially
when the optimal operator set required for a particular task is missing from
the library. In order to address these issues and achieve an ultimate
heterogeneity level to boost the network diversity along with computational
efficiency, in this study we propose Self-organized ONNs (Self-ONNs) with
generative neurons that have the ability to adapt (optimize) the nodal operator
of each connection during the training process. Therefore, Self-ONNs can have
an utmost heterogeneity level required by the learning problem at hand.
Moreover, this ability voids the need of having a fixed operator set library
and the prior operator search within the library in order to find the best
possible set of operators. We further formulate the training method to
back-propagate the error through the operational layers of Self-ONNs.Comment: 14 pages, 14 figures, journal articl
Blind ECG Restoration by Operational Cycle-GANs
Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this pilot study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. Methods: To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. Results: The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis. Significance: As a pioneer study in ECG restoration, the corrupted ECG signals can be restored to clinical level quality. Conclusion: By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve.publishedVersionPeer reviewe
Operational Neural Networks
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the
so-called Multi-Layer Perceptrons (MLPs) are well-known universal
approximators. However, their learning performance varies significantly
depending on the function or the solution space that they attempt to
approximate. This is mainly because of their homogenous configuration based
solely on the linear neuron model. Therefore, while they learn very well those
problems with a monotonous, relatively simple and linearly separable solution
space, they may entirely fail to do so when the solution space is highly
nonlinear and complex. Sharing the same linear neuron model with two additional
constraints (local connections and weight sharing), this is also true for the
conventional Convolutional Neural Networks (CNNs) and, it is, therefore, not
surprising that in many challenging problems only the deep CNNs with a massive
complexity and depth can achieve the required diversity and the learning
performance. In order to address this drawback and also to accomplish a more
generalized model over the convolutional neurons, this study proposes a novel
network model, called Operational Neural Networks (ONNs), which can be
heterogeneous and encapsulate neurons with any set of operators to boost
diversity and to learn highly complex and multi-modal functions or spaces with
minimal network complexity and training data. Finally, a novel training method
is formulated to back-propagate the error through the operational layers of
ONNs. Experimental results over highly challenging problems demonstrate the
superior learning capabilities of ONNs even with few neurons and hidden layers.Comment: 21 page
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity. Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported.publishedVersionPeer reviewe