Jose Giraldo

Jose Giraldo

Sound engineer

Publications

Big Data Analysis and Machine Learning Techniques to Answer New Demands on Long Term Monitoring Noise Analysis

Published in Internoise-Madrid, 2019

This paper presents the combination of different noise monitoring systems (conventional and low-cost), combined with data analysis procedures to manage the big amount of data coming from long term acoustic monitoring in real world cases. Signal processing and machine learning techniques are used to solve higher requirements in noise monitoring. Direct measurements are difficult and expensive for many situations due to non-permanent sources in urban areas and conventional monitoring systems just giving noise levels information are not enough to identify the noise sources along time

Recommended citation: Big Data Analysis and Machine Learning Techniques to Answer New Demands on Long Term Monitoring Noise Analysis Bañuelos-Arteagoitia, Oier; Giraldo-Valencia, José Omar; Bañuelos-Irusta, Alberto; Gómez-Arteagoitia, Aritz http://www.sea-acustica.es/fileadmin/INTERNOISE_2019/Fchrs/Proceedings/1749.pdf

Calibration of low-cost sound level meters using machine learning techniques(Abstract accepted)

Published in The Journal of the Acoustical Society of America, 2018

the use of reinforcement learning techniques is explored to calibrate the sound pressure levels generated by a low-cost monitoring device using a class one sound level meter as reference in a continuous measurement setup.

Recommended citation: Giraldo, Jose. (2018). "Calibration of low-cost sound level meters using machine learning techniques." The Journal of the Acoustical Society of America. 1(1). https://asa.scitation.org/doi/10.1121/1.5036424

Automatic Sound event recognition of traffic and community noise

Published in Universidad de San Buenaventura, 2017

Audio content analysis of real world recordings instead of common SPL measures is proposed as a technique for environmental noise evaluation. However, because of the large quantity of data that a monitoring system produces an automatic sound event algorithm was built using audio descriptors as MFCC, spectral centroid, zcr and roll off coupled with a supervised learning approach with SVM

Recommended citation: Giraldo, Jose. (2017). "Automatic Sound event recognition of traffic and community noise." Universidad de San Buenaventura. 1(1). http://bibliotecadigital.usb.edu.co/bitstream/10819/4431/1/Identificacion_Automatica_Eventos_Giraldo_2017.pdf