Signal segmentation and its application in the feature extraction of speech

AbstractAvian survey is a time-consuming and challenging task, often being conducted in remote and sometimes inhospitable locations. In this context, the development of automated acoustic landscape monitoring systems for bird survey is essential. We conducted a comparative study between two machine learning methods for the detection and identification of 2 endangered Brazilian bird species from the Psittacidae species, the Amazona brasiliensis and the Amazona vinacea. Specifically, we focus on the identification of these 2 species in an acoustic landscape where similar vocalizations from other Psittacidae species are present. A 3-step approach is presented, composed of signal segmentation and filtering, feature extraction, and classification. In the feature extraction step, the Mel-Frequency Cepstrum Coefficients features were extract and fed to the Random Forest Algorithm and the Multilayer Perceptron for training and classifying acoustic samples. The experiments showed promising results, particularly for the Random Forest algorithm, achieving accuracy of up to 99%. Using a combination of signal segmentation and filtering before the feature extraction steps greatly increased experimental results. Additionally, the results show that the proposed approach is robust and flexible to be adopted in passive acoustic monitoring systems.

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Multi-channel EEG signal segmentation and feature extraction

2010 IEEE 14th International Conference on Intelligent Engineering Systems ◽

10.1109/ines.2010.5483824 ◽

2010 ◽

Cited By ~ 10

Author(s):

Ales Prochazka ◽

Martina Mudrova ◽

Oldrich Vysata ◽

Robert Hava ◽

Carmen Paz Suarez Araujo

Keyword(s):

Feature Extraction ◽

Eeg Signal ◽

Signal Segmentation

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Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor Manufacturing

Sensors ◽

10.3390/s21113880 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3880

Author(s):

Kyuchang Chang ◽

Youngji Yoo ◽

Jun-Geol Baek

Keyword(s):

Time Series ◽

Feature Extraction ◽

Anomaly Detection ◽

Semiconductor Manufacturing ◽

Time Series Data ◽

Series Data ◽

Signal Segmentation ◽

Sensor Signals ◽

Two Phases ◽

One Class Classification

This paper proposes a new diagnostic method for sensor signals collected during semiconductor manufacturing. These signals provide important information for predicting the quality and yield of the finished product. Much of the data gathered during this process is time series data for fault detection and classification (FDC) in real time. This means that time series classification (TSC) must be performed during fabrication. With advances in semiconductor manufacturing, the distinction between normal and abnormal data has become increasingly significant as new challenges arise in their identification. One challenge is that an extremely high FDC performance is required, which directly impacts productivity and yield. However, general classification algorithms can have difficulty separating normal and abnormal data because of subtle differences. Another challenge is that the frequency of abnormal data is remarkably low. Hence, engineers can use only normal data to develop their models. This study presents a method that overcomes these problems and improves the FDC performance; it consists of two phases. Phase I has three steps: signal segmentation, feature extraction based on local outlier factors (LOF), and one-class classification (OCC) modeling using the isolation forest (iF) algorithm. Phase II, the test stage, consists of three steps: signal segmentation, feature extraction, and anomaly detection. The performance of the proposed method is superior to that of other baseline methods.

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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