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In many high-impact applications, it is important to ensure the quality of the output of a machine learning algorithm as well as its reliability in comparison to the complexity of the algorithm used. In this paper, we have initiated a mathematically rigorous theory to decide which models (algorithms applied on data sets) are close to each other in terms of certain metrics, such as performance and the complexity level of the algorithm. This involves creating a grid on the hypothetical spaces of data sets and algorithms so as to identify a finite set of probability distributions from which the data sets are sampled and a finite set of algorithms. A given threshold metric acting on this grid will express the nearness (or statistical distance) of each algorithm and data set of interest to any given application. A technically difficult part of this project is to estimate the so-called metric entropy of a compact subset of functions of \textbf{infinitely many variables} that arise in the definition of these spaces.
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This content will become publicly available on April 6, 2026
CorrGAN: Simultaneous Learning of Speech Enhancement and Perceptual Quality Loss Functions
Deep-learning models have allowed effective end-to-end SE systems in the Speech Enhancement (SE) field. Most of these methods are trained using a fixed reconstruction loss in a supervised setting. Often these losses do not perfectly represent the desired perceptual quality metrics, resulting in sub-optimal performance. Recently, there have been efforts to learn the behavior of those metrics directly via neural nets for training SE models. However, an accurate estimation of the true metric function introduces statistical complexity for training because it attempts to capture the exact value of the metric. We propose an adversarial training strategy based on statistical correlation that avoids the complexity of estimating the SE metric while learning to mimic its overall behavior. We call this framework CorrGAN and show its significant improvement over standard losses of the SOTA baselines and achieve SOTA performance on the VoiceBank+DEMAND dataset.
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- PAR ID:
- 10616198
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6874-1
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Hyderabad, India
- Sponsoring Org:
- National Science Foundation
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