Computer Experimentation on Echo Detection, Using the Cepstrum and Pseudoautocovariance

Bogert, B. P.; Ossanna, J. F.

doi:10.1121/1.1942899

Cited by 5 publications

(4 citation statements)

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“…These parameters approximate the human way of perceiving a piece of music by using the Mel scale and contain information about the timbre and texture of the musical piece. They were initially used in echo detection in seismic waves [42], but it has also found application in speech detection issues [43]. MFCCs have also been successfully applied to determine the genre and timbre of a musical piece [44].…”

Section: Considered Featuresmentioning

confidence: 99%

Attributes Relevance in Content-Based Music Recommendation System

Kostrzewa,

Chrobak,

Brzeski

2024

Applied Sciences

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The possibility of recommendations of musical songs is becoming increasingly required because of the millions of users and songs included in online databases. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using three basic factors based on genre classification made by neural network, Mel-frequency cepstral coefficients (MFCCs), and the tempo of the song. The recommendation system is built using a probability function based on these three factors. The authors’ contribution to the development of an automatic content-based recommendation system are methods built with the use of the mentioned three factors. Using different combinations of them, four strategies were created. All four strategies were evaluated based on the feedback score of 37 users, who created a total of 300 surveys. The proposed recommendation methods show a definite improvement in comparison with a random method. The obtained results indicate that the MFCC parameters have the greatest impact on the quality of recommendations.

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Section: Considered Featuresmentioning

confidence: 99%

Attributes Relevance in Content-Based Music Recommendation System

Kostrzewa,

Chrobak,

Brzeski

2024

Applied Sciences

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“…This method essentially performs secondary spectrum analysis of the signal spectrum, providing a powerful tool for identifying complex periodic components in the signal spectrum (Li et al, 2006a). The term "cepstrum" was first introduced by Bogert (1963), along with various definitions emerging with the advent of the fast Fourier-transform (FFT) algorithm (Randall and Sawalhi, 2011). Currently, it has been widely applied in fields such as speech (Li et al, 2006b), acoustics (Zeng et al, 2011), mechanical fault diagnosis (Choi and Kim, 2007), and sound detection and classification (Li, 2012).…”

Section: Introductionmentioning

confidence: 99%

Data processing method for magnetotelluric sounding based on cepstral analysis

Zhan,

Liu,

Liu

et al. 2023

Front. Earth Sci.

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Magnetotelluric (MT) signals exhibit the characteristics of being weak and having a wide frequency band. The acquired field data are susceptible to various types of noise, which poses challenges in accurate identification and processing. Currently, there exist numerous MT data processing methods; however, they lack efficiency and physical meaning. To address this issue and improve the signal-to-noise ratio of the acquired data, this study proposes a MT data processing method based on cepstral analysis. By employing cepstral analysis on the MT data, the cepstrum is obtained, and an appropriate truncation position is selected for processing. The experimental results demonstrate that this method obtains smoother and more continuous apparent resistivity curves with fewer errors. Compared with other methods, the cepstral analysis method can effectively suppress different types of MT noise, and the method is simple and efficient with clear physical significance.

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“…The cepstrum is an anagram of 'spectrum', and is a technique for non-linear signals that is used in a variety of applications. This function, introduced by Bogert and Ossanna [20] has the ability to identify families of frequency harmonics associated with pressure wave reflections from the leak. The method not only detects leaks but also can also estimate their severity.…”

Section: Introductionmentioning

confidence: 99%

Leak detection in pipeline networks using low-profile piezoceramic transducers

Taghvaei

Beck

Staszewski

2007

Struct. Control Health Monit.

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Detecting leaks in pipeline networks such as water distribution systems is an important, yet difficult task. Various techniques have been developed for leak detection, but so far none has provided a suitable solution for industrial applications, as they have low performance or high cost.An experimental method based on pressure measurements has previously been developed to identify leaks which involve first removing the noise with orthogonal wavelets and then applying cepstrum analysis to identify features in the physical network. This previous work has shown that this approach is capable of both locating the position and estimating the severity of leaks in pipeline networks.This study involves an experiment with a simple fluid filled pipeline network. A solenoid valve is used to introduce a pressure wave into the network. By periodically opening and closing the valve the wave propagates in the fluid. This pressure wave is then sensed by a low-profile piezoceramic transducer. A conventional pressure transducer is also used for comparative studies. The experiment is performed for different pipe lengths and severities of leakage.This work shows that while the piezoceramic transducer is poor at recording accurate pressure histories, it is perfectly acceptable for this type of condition monitoring, being able to identify the position and severity of leaks and other features in the pipeline network.

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Computer Experimentation on Echo Detection, Using the Cepstrum and Pseudoautocovariance

Cited by 5 publications

References 0 publications

Attributes Relevance in Content-Based Music Recommendation System

Attributes Relevance in Content-Based Music Recommendation System

Data processing method for magnetotelluric sounding based on cepstral analysis

Leak detection in pipeline networks using low-profile piezoceramic transducers

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