Abstract
Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series' target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % +/- 1.2 and an F1-score of 95.4 % +/- 1.3.

Abstract
Keeping high service levels of a fast-growing number of servers is crucial and challenging for IT operations teams. Online monitoring systems trigger many occurrences that experts find hard to keep up with. In addition, most of the triggered warnings do not correspond to real, critical problems, making it difficult for technicians to know which to focus on and address in a timely manner. Outlier and concept drift detection techniques can be applied to multiple streams of readings related to server monitoring metrics, but they also generate many False Positives. Ranking algorithms can already prioritize relevant results in information retrieval and recommender systems. However, these approaches are supervised, making them inapplicable in event detection on data streams. We propose a framework that combines event aggregations and uses a customized clustering algorithm to score and rank alarms in the context of IT operations. To the best of our knowledge, this is the first unsupervised, online, high-dimensional approach to rank IT ops events and contributes to advancing knowledge about associated key concepts and challenges of this problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
In this paper we focus on the real-time frequency domain analysis of speech signals, and on the extraction of suitable and perceptually meaningful features that are related to the glottal source and that may pave the way for robust speaker identification and voice register classification. We take advantage of an analysis-synthesis framework derived from an audio coding algorithm in order to estimate and model the relative delays between the different harmonics reflecting the contribution of the glottal source and the group delay of the vocal tract filter. We show in this paper that this approach effectively captures the shape invariance of a periodic signal and may be suited to monitor and extract in real-time perceptually important features correlating well with specific voice registers or with a speaker unique sound signature. A first validation study is described that confirms the competitive performance of the proposed approach in the automatic classification of the breathy, normal and pressed voice phonation types.

Abstract
In this paper, an accurate method that estimates the HNR from sustained vowels based on harmonic structure modeling is proposed. Basically, the proposed algorithm creates an accurate harmonic structure where each harmonic is parameterized by frequency, magnitude and phase. The harmonic structure is then synthesized and assumed as the harmonic component of the speech signal. The noise component can be estimated by subtracting the harmonic component from the speech signal. The proposed algorithm was compared to others HNR extraction algorithms based on spectral, cepstral and time domain methods, and using different performance measures.

Abstract
In this paper we address the accurate estimation of the frequency of sinusoids of natural signals such as singing, voice or music. These signals are intrinsicly harmonic and are normally contaminated by noise. Taking the Cramér-Rao Lower Bound for unbiased frequency estimators as a reference, we compare the performance of several DFT-based frequency estimators that are non-iterative and that use the rectangular window or the Hanning window. Tests conditions simulate harmonic interference and two new ArcTan-based frequency estimators are also included in the tests. Conclusions are presented on the relative performance of the different frequency estimators as a function of the SNR. ©2010 IEEE.

Abstract
The accurate estimation of the frequency of sinusoids is a frequent problem in many signal processing problems including the real-time analysis of the singing voice. In this paper we rely on a single DFT magnitude spectrum in order to perform frequency estimation in a non-iterative way. Two new frequency estimation methods are derived that are matched to the time analysis window and that reduce the maximum absolute estimation error to about 0.1% of the bin width of the DFT. The performance of these methods is evaluated including the parabolic method as a reference, and considering the influence of noise. A combined model is proposed that offers higher noise robustness than that of a single model. ©2010 IEEE.