Ángel Ramos-Ridao

Application of a methodology for categorizing and differentiating urban soundscapes using acoustical descriptors and semantic-differential attributes

A subjective and physical categorization of an ambient sound is the first step to evaluate the soundscape and provides a basis for designing or adapting this ambient sound to match peoples expectations. For this reason, the main goal of this work is to develop a categorization and differentiation analysis of soundscapes on the basis of acoustical and perceptual variables. A hierarchical cluster analysis, using 15 semantic-differential attributes and acoustical descriptors to include an equivalent sound-pressure level, maximum-minimum sound-pressure level, impulsiveness of the sound-pressure level, sound-pressure level time course, and spectral composition, was conducted to classify soundscapes into different typologies. This analysis identified 15 different soundscape typologies. Furthermore, based on a discriminant analysis the acoustical descriptors, the crest factor (impulsiveness of the sound-pressure level), and the sound level at 125 Hz were found to be the acoustical variables with the highest impact in the differentiation of the recognized types of soundscapes. Finally, to determine how the different soundscape typologies differed from each other, both subjectively and acoustically, a study was performed.

A neural network based model for urban noise prediction.

Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then, most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context, urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise, using a wide range of approaches, but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques, namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy, and thus, was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results, the values obtained were also quite acceptable.

A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match peoples expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).

Estimation Procedure of the Descriptor LAeq,T from the Stabilization Time of the Sound Pressure Level Value

Temporal structure of sound pressure level is a key aspect at the time of characterizing urban sound environments. In urban agglomerations, environmental noise levels fluctuate over a large range as a result of the great complexity of these settings, with considerable temporal and spatial heterogeneity. Furthermore, the domain in urban environments of noise sources, such as road traffic, commercial or leisure activities, construction works, etc., together with the occurrence of sudden sound-level maxima events (bells, sirens, vehicles at high traffic speed, honking horns…), which are quite frequent in urban agglomerations, generate the appearance of very high values of the impulsiveness of sound pressure level. This aspect causes a great influence on the time necessary for environmental noise levels to become stabilized, which is a key aspect for the accurate measurement, interpretation and guarantee of a statistically representative sample of a given urban sound environment. Therefore, the goal pursued in this work is to put forth a procedure for the calculation of a value of LAeqT, representative of a certain urban location in a short-term time period, from the utilization of the value of the stabilization time of the sound pressure level.

Efficiency of a biological aerated filter for the treatment of leachate produced at a landfill receiving non-recyclable waste

The feasibility of a biological aerated filter for the treatment of a partially stabilized leachate from a landfill receiving non-recyclable wastes was assessed in laboratory-scale experiments. Maximum COD, BOD5 and TSS removal efficiencies achievable by the biofilter as well as the optimal hydraulic and organic loading rates were determined by laboratory-scale tests in batch and continuous mode. Experiments in batch mode which lasted for 7 days showed that COD and BOD5 removal efficiencies were stabilized after the second day of operation and kept at around 56–60% and 83–97%, respectively, for the rest of the period studied. The remaining fraction (approximately 40% of the COD) was found to be composed of recalcitrant or not easily biodegradable compounds. The COD and BOD5 removal efficiencies decreased with increasing hydraulic loading rates. The plant worked under optimal conditions at hydraulic loading rates of 0.71 and 1.41 m3/m2d (hydraulic retention times of 15.95 and 7.97 h, respectively) and at COD loading rates below 14 kg COD/m3, where COD removal efficiencies were around 60%. TSS removal efficiencies were not significantly influenced by the hydraulic loading rate. The results obtained demonstrated the feasibility of a biological aerated filter for the removal of the biodegradable fraction of the organic matter contained in the leachate. However, a physicochemical process was found to be necessary as pre- or post-treatment for the removal of the recalcitrant fraction.

Developing an Artificial Neural Network for Modeling and Prediction of Temporal Structure and Spectral Composition of Environmental Noise in Cities

Noise pollution in large cities is an ever-growing problem, due to several factors: the increase in demographic density, the increase in the number of per capita devices, appliances and vehicles capable of generating loud noise, and the fact that society is getting used to higher noise levels. One of the most important factors that help us to explain this fact is the road traffic, since as is generally established, road traffic is the most important and generalized sound source in the urban zones of the developed countries. Generally speaking, this one is also, with difference, the sound source that produces more disturbances and nuisances on the urban residents. However, road traffic is not the only noisy source in urban environments: other noisy sources relating to construction work, commercial activity, recreation, etc. have been found. At the same time, sound spaces where road traffic does not have a direct incidence and in which natural and social sounds predominate, e.g. green areas, can be observed (Torija et al., 2010a). The European Directive 2002/49/EC on the Assessment and Management of Environmental Noise aims to create a common framework for assessing exposure to environmental noise in all Member States. With the use of indicators and evaluation methods harmonized the results will be grouped into strategic maps. These maps are designed to comprehensively assess noise exposure in a given area, or for overall predictions in that area. In addition, they will be the basis for developing both action plans and strategies in the fight against noise (Directive 2002/49/EC). For the development of assessment and achievement of the objectives stated in the above mentioned directive, from the European Commission the methods used to predict different emission sources present in urban environments (industrial noise, road traffic, railway traffic and aircraft traffic) are recommended (Commission Recommendation 2003/613/EC). All these methods are based only on the obtaining of the A-weighted energy-equivalent sound pressure level (LAeq). Nevertheless, any physical characterization of a sound environment calls not only for consideration of the A-weighted sound pressure level (LAeq), but also requires description of the temporal structure and spectral composition of the sound (Berglund & Nilsson, 2001; Botteldooren et al., 2006). These factors bear great weight in the perception of noise (Viollon & Lavandier, 2000; Berglund & Nilsson, 2001;