Change Point Determination in Audio Data Using Auditory Features

Abstract The study is aimed to investigate the properties of auditory-based features for audio change point detection process. In the performed analysis, two popular techniques have been used: a metric-based approach and the ΔBIC scheme. The efficiency of the change point detection process depends on the type and size of the feature space. Therefore, we have compared two auditory-based feature sets (MFCC and GTEAD) in both change point detection schemes. We have proposed a new technique based on multiscale analysis to determine the content change in the audio data. The comparison of the two typical change point detection techniques with two different feature spaces has been performed on the set of acoustical scenes with single change point. As the results show, the accuracy of the detected positions depends on the feature type, feature space dimensionality, detection technique and the type of audio data. In case of the ΔBIC approach, the better accuracy has been obtained for MFCC feature space in the most cases. However, the change point detection with this feature results in a lower detection ratio in comparison to the GTEAD feature. Using the same criteria as for ΔBIC, the proposed multiscale metric-based technique has been executed. In such case, the use of the GTEAD feature space has led to better accuracy. We have shown that the proposed multiscale change point detection scheme is competitive to the ΔBIC scheme with the MFCC feature space.

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Accuracy improvement of multi-stage change-point detection scheme by weighting alerts based on false-positive rate

2009 IEEE International Workshop Technical Committee on Communications Quality and Reliability ◽

10.1109/cqr.2009.5137356 ◽

2009 ◽

Cited By ~ 1

Author(s):

Yukinobu Fukushima ◽

Tutomu Murase ◽

Ryohei Fujimaki ◽

Syunsuke Hirose ◽

Tokumi Yokohira

Keyword(s):

Change Point ◽

False Positive ◽

False Positive Rate ◽

Change Point Detection ◽

Accuracy Improvement ◽

Detection Scheme ◽

Multi Stage ◽

Positive Rate ◽

Stage Change ◽

Point Detection

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Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Sensors ◽

10.3390/s20010310 ◽

2020 ◽

Vol 20 (1) ◽

pp. 310 ◽

Cited By ~ 2

Author(s):

Cristian Culman ◽

Samaneh Aminikhanghahi ◽

Diane J. Cook

Keyword(s):

Change Point ◽

Recognition Performance ◽

Sampling Rate ◽

Activity Monitoring ◽

Change Point Detection ◽

Continuous Sampling ◽

Detection Techniques ◽

Transition Times ◽

Point Detection ◽

Complex Activities

Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.

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The Role of Mobility and Sanitary Measures on the Delay of Community Transmission of COVID-19 in Costa Rica

Epidemiologia ◽

10.3390/epidemiologia2030022 ◽

2021 ◽

Vol 2 (3) ◽

pp. 294-304

Author(s):

Luis A. Barboza ◽

Paola Vásquez ◽

Gustavo Mery ◽

Fabio Sanchez ◽

Yury E. García ◽

...

Keyword(s):

Costa Rica ◽

Change Point ◽

Human Mobility ◽

Change Point Detection ◽

Population Mobility ◽

Detection Techniques ◽

Community Transmission ◽

Parametric Change ◽

Point Detection

The aim of this paper is to infer the effects that change on human mobility had on the transmission dynamics during the first four months of the SARS-CoV-2 pandemic in Costa Rica, which could have played a role in delaying community transmission in the country. First, by using parametric and non-parametric change-point detection techniques, we were able to identify two different periods when the trend of daily new cases significantly changed. Second, we explored the association of these changes with data on population mobility. This also allowed us to estimate the lag between changes in human mobility and rates of daily new cases. The information was then used to establish an association between changes in population mobility and the sanitary measures adopted during the study period. Results showed that during the initial two months of the pandemic in Costa Rica, the implementation of sanitary measures and their impact on reducing human mobility translated to a mean reduction of 54% in the number of daily cases from the projected number, delaying community transmission.

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Change Point Detection Techniques

The Kluwer International Series in Engineering and Computer Science - Information-Statistical Data Mining ◽

10.1007/978-1-4419-9001-3_7 ◽

2004 ◽

pp. 93-98

Author(s):

Bon K. Sy ◽

Arjun K. Gupta

Keyword(s):

Change Point ◽

Change Point Detection ◽

Detection Techniques ◽

Point Detection

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Change-point detection-based power quality monitoring in smart grids

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2015.7 ◽

2015 ◽

Vol 4 ◽

Author(s):

Xingze He ◽

Man-On Pun ◽

C.C. Jay Kuo

Keyword(s):

Power Quality ◽

Change Point ◽

Smart Grids ◽

Economic Loss ◽

Change Point Detection ◽

Quality Monitoring ◽

Detection Scheme ◽

Power Quality Monitoring ◽

Point Detection ◽

Short Time

The enormous economic loss caused by power quality problems (more than $ 150 billion per year in USA) makes power quality monitoring an important component in power grid. With highly connected fragile digital equipment and appliances, Smart Grid has more stringent timeliness and reliability requirements on power quality monitoring. In this work, we propose a change-point detection theory-based power quality monitoring scheme to detect the most detrimental power quality events, such as voltage sags, transients and swells in a quick and reliable manner. We first present a method for single-sensor detection scenario. Based on that, we extend the scheme to multi-sensor joint detection scheme which further enhances the detection performance. A group of conventional power quality monitoring schemes (i.e. Root-mean-square, Short-time Fourier transform, MUSIC, and MBQCUSUM) are compared with the proposed scheme. Experimental results assert the superior of the proposed scheme in terms of detection latency and robustness.

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Change Point Detection Techniques on Seismicity Models

Statistical Methods and Modeling of Seismogenesis ◽

10.1002/9781119825050.ch10 ◽

2021 ◽

pp. 271-298

Author(s):

Rodi Lykou ◽

George Tsaklidis

Keyword(s):

Change Point ◽

Change Point Detection ◽

Detection Techniques ◽

Seismicity Models ◽

Point Detection

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Ensemble binary segmentation for irregularly spaced data with change-points

Journal of the Korean Statistical Society ◽

10.1007/s42952-021-00120-w ◽

2021 ◽

Author(s):

Karolos K. Korkas

Keyword(s):

Time Series ◽

Change Point ◽

R Package ◽

Transformation Process ◽

Change Point Detection ◽

Change Points ◽

Segmentation Method ◽

Binary Segmentation ◽

A New Technique ◽

Point Detection

AbstractWe propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the ensemble binary segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the binary segmentation method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. We do not restrict the total number of change-points a time series can have, therefore, our proposed method works well when the spacings between change-points are short. Our main change-point detection statistic is the time-varying autoregressive conditional duration model on which we apply a transformation process in order to decorrelate it. To examine the performance of EBS we provide a simulation study for various types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package , available to download from CRAN.

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