Machine learning models and neural networks in particular can be used in the field of image processing. Machine learning models are trained to better improve their output quality. During training, domain generalization is the problem of making accurate predictions on previously unseen domains, especially when these domains are very different from...

The prevalence of relational data in several real-world applications, e.g., social network analysis, recommendation systems, and neurological modeling has led to crucial advances in machine learning techniques for graph-structured data. This encompasses a wide-range of formulations to mine and gather insights from complex network datasets — node...

­Background Audio source separation, the process of recovering constituent source signals from a given audio mixture, is a key component in downstream applications such as audio enhancement and music information retrieval. Modern under-determined audio source separation systems rely on supervised training of carefully tailored neural network architectures...

Background Audio source separation refers to the process of extracting constituent sources from a given audio mixture. Despite being a critical component of audio enhancement and retrieval systems, the task of source separation is severely challenged by variabilities in acoustic conditions and the highly ill-posed nature of this inverse problem. A...

Background The implementation of deep learning in a wide range of applications has resulted in an ever-increasing complexity of neural network architecture. These network architectures require reconfiguration to support specific applications, particularly in the field of computer vision where an image may contain a variety of objects needing to be...

Background In automatic speech processing systems, speaker diarization is a crucial front-end component for separating speech segments by speaker without a priori knowledge about speaker identities. The first phase of many state-of-the-art diarization techniques involves the conversion of original speech data into representative i-vectors, which are...

In many resource-constrained and noisy scenarios, the process of sensing as well as transmitting images introduces various forms of degradations. For example, in Magnetic Resonance Imaging (MRI), the lengthy sensing process may result in blurry images due to subtle movements of the patient. Bandwidth limitations and noisy channels may introduce degradation...