Abstract Voice activity detection (VAD) based on deep neural networks (DNN) have demonstrated good performance in adverse acoustic environments. Current DNN-based VAD optimizes a surrogate function, e.g., minimum cross-entropy or squared error, at given decision threshold. However, usually works on-the-fly with dynamic threshold, and the receiver operating characteristic (ROC) curve is global e...

In this paper, we report on the process of preparing and configuring the AUC Schlumbeger cluster to join two grid communities in SC2002 and SC2003. It is also to report on the role of Globus; Grid manifesto protocol, in grid enabling the cluster to compete in the IEEE-ACM Super Computing Challenges’ test beds, which were held in Maryland 2002, and Phoenix 2003. The paper refers to the results a...

AUC (Area Under ROC Curve) has been an important criterion widely used in diverse learning tasks. To optimize AUC, many learning approaches have been developed, most working with pairwise surrogate losses. Thus, it is important to study the AUC consistency based on minimizing pairwise surrogate losses. In this paper, we introduce the generalized calibration for AUC optimization, and prove that ...

AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in diverse learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank and information retrieval. Many learning approaches are developed to optimize AUC, whereas owing to its non-convexity and discontinuousness, almost all approaches work with surrogate loss functions. T...

The area under the ROC curve (AUC) has been widely used to measure ranking performance for binary classification tasks. AUC only employs the classifier’s scores to rank the test instances; thus, it ignores other valuable information conveyed by the scores, such as sensitivity to small differences in the score values. However, as such differences are inevitable across samples, ignoring them may ...

Area under the receiver operating characteristics curve (AUC) is a popular measure for evaluating the quality of binary classifiers, and intuitively, machine learning algorithms that maximize an approximation of AUC should have a good AUC performance when classifying new examples. However, designing such algorithms in the framework of kernel methods has proven to be challenging. In this paper, ...

Learning for maximizing AUC performance is an important research problem in Machine Learning and Artificial Intelligence. Unlike traditional batch learning methods for maximizing AUC which often suffer from poor scalability, recent years have witnessed some emerging studies that attempt to maximize AUC by single-pass online learning approaches. Despite their encouraging results reported, the ex...

1 AUC Estimation The area under the ROC curve can be approximated using lower rectangles, upper rectangles or by using a linear approximation, as shown in Figure 1. The expressions related to each of these approximations are Al(zA) = M−1 ∑ t=0 (1− M̂Rt)(F̂ARt+1 − F̂ARt) Au(zA) = M−1 ∑ t=0 (1− M̂Rt+1)(F̂ARt+1 − F̂ARt) Am(zA) = M−1 ∑ t=0 (1− M̂Rt + M̂Rt+1 2 )(F̂ARt+1 − F̂ARt). Substituting the estimates fo...