Separation and identification of environmental noise signals using independent component analysis and data mining techniques

Ma Guadalupe López P.; Luis P. Sánchez F.; Heron Molina Lozano; L. Noé Oliva Moreno

doi:10.1109/CERMA.2011.21

Separation and identification of environmental noise signals using independent component analysis and data mining techniques

Ma Guadalupe López P., Luis P. Sánchez F., Heron Molina Lozano, L. Noé Oliva Moreno

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In the present work, we show a way to separate noise signals recorded with microphones industrial, in order that they can be analyzed separately. Blind Source Separation is accomplished using Independent Component Analysis (ICA) technique in the wavelet domain. Also, it is necessary to identify the separate sources, taking into account that each signal separate has some components of the signals belonging to the initial mixture. Through data mining techniques and characteristic features of the signals obtained are derived rules in order to identify the main source that is present in the mix, for this we propose the use of data mining techniques. The results show a substantial improvement in the separation of mixtures of real environmental noise using ICA, although the mixtures are not fully independent.

Original language	English
Title of host publication	Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011
Pages	83-88
Number of pages	6
DOIs	https://doi.org/10.1109/CERMA.2011.21
State	Published - 2011
Event	2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011 - Cuernavaca, Morelos, Mexico Duration: 15 Nov 2011 → 18 Nov 2011

Publication series

Name	Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011

Conference

Conference	2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011
Country/Territory	Mexico
City	Cuernavaca, Morelos
Period	15/11/11 → 18/11/11

Keywords

audio signal
blind source separation
data mining
independent component analysis

Access to Document

10.1109/CERMA.2011.21

Cite this

López P., M. G., Sánchez F., L. P., Lozano, H. M., & Moreno, L. N. O. (2011). Separation and identification of environmental noise signals using independent component analysis and data mining techniques. In Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011 (pp. 83-88). Article 6125803 (Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011). https://doi.org/10.1109/CERMA.2011.21

López P., Ma Guadalupe ; Sánchez F., Luis P. ; Lozano, Heron Molina et al. / Separation and identification of environmental noise signals using independent component analysis and data mining techniques. Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011. 2011. pp. 83-88 (Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011).

@inproceedings{8b9c1281871f4f43b0e65b1e721911eb,

title = "Separation and identification of environmental noise signals using independent component analysis and data mining techniques",

abstract = "In the present work, we show a way to separate noise signals recorded with microphones industrial, in order that they can be analyzed separately. Blind Source Separation is accomplished using Independent Component Analysis (ICA) technique in the wavelet domain. Also, it is necessary to identify the separate sources, taking into account that each signal separate has some components of the signals belonging to the initial mixture. Through data mining techniques and characteristic features of the signals obtained are derived rules in order to identify the main source that is present in the mix, for this we propose the use of data mining techniques. The results show a substantial improvement in the separation of mixtures of real environmental noise using ICA, although the mixtures are not fully independent.",

keywords = "audio signal, blind source separation, data mining, independent component analysis",

author = "{L{\'o}pez P.}, {Ma Guadalupe} and {S{\'a}nchez F.}, {Luis P.} and Lozano, {Heron Molina} and Moreno, {L. No{\'e} Oliva}",

year = "2011",

doi = "10.1109/CERMA.2011.21",

language = "Ingl{\'e}s",

isbn = "9780769545639",

series = "Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011",

pages = "83--88",

booktitle = "Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011",

note = "2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011 ; Conference date: 15-11-2011 Through 18-11-2011",

}

López P., MG, Sánchez F., LP , Lozano, HM & Moreno, LNO 2011, Separation and identification of environmental noise signals using independent component analysis and data mining techniques. in Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011., 6125803, Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011, pp. 83-88, 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011, Cuernavaca, Morelos, Mexico, 15/11/11. https://doi.org/10.1109/CERMA.2011.21

Separation and identification of environmental noise signals using independent component analysis and data mining techniques. / López P., Ma Guadalupe; Sánchez F., Luis P.; Lozano, Heron Molina et al.
Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011. 2011. p. 83-88 6125803 (Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Separation and identification of environmental noise signals using independent component analysis and data mining techniques

AU - López P., Ma Guadalupe

AU - Sánchez F., Luis P.

AU - Lozano, Heron Molina

AU - Moreno, L. Noé Oliva

PY - 2011

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N2 - In the present work, we show a way to separate noise signals recorded with microphones industrial, in order that they can be analyzed separately. Blind Source Separation is accomplished using Independent Component Analysis (ICA) technique in the wavelet domain. Also, it is necessary to identify the separate sources, taking into account that each signal separate has some components of the signals belonging to the initial mixture. Through data mining techniques and characteristic features of the signals obtained are derived rules in order to identify the main source that is present in the mix, for this we propose the use of data mining techniques. The results show a substantial improvement in the separation of mixtures of real environmental noise using ICA, although the mixtures are not fully independent.

AB - In the present work, we show a way to separate noise signals recorded with microphones industrial, in order that they can be analyzed separately. Blind Source Separation is accomplished using Independent Component Analysis (ICA) technique in the wavelet domain. Also, it is necessary to identify the separate sources, taking into account that each signal separate has some components of the signals belonging to the initial mixture. Through data mining techniques and characteristic features of the signals obtained are derived rules in order to identify the main source that is present in the mix, for this we propose the use of data mining techniques. The results show a substantial improvement in the separation of mixtures of real environmental noise using ICA, although the mixtures are not fully independent.

KW - audio signal

KW - blind source separation

KW - data mining

KW - independent component analysis

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BT - Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011

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López P. MG, Sánchez F. LP , Lozano HM , Moreno LNO. Separation and identification of environmental noise signals using independent component analysis and data mining techniques. In Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011. 2011. p. 83-88. 6125803. (Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011). doi: 10.1109/CERMA.2011.21

Separation and identification of environmental noise signals using independent component analysis and data mining techniques

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