No abstract

This study aims to explore whether and howu rban morphology influences the capability of ar esidential area on attenuating trafficn oise levels. Particular attention is paid to low-density residential areas, which are more appealing for study because of their relatively lowcapability of noise resistance compared with the high-density morphology.S ix urban morphological parameters that are accessible and commonly used in urban design and planning are selected. Noise mapping techniques have been employed and aMAT LAB program has been developed to obtain the spatial noise levelindices, L n .The relationships of urban morphological parameters with the spatial noise levela ttenuation and the size of noisy areas were subsequently revealed. The results indicate that the spatial noise levelattenuation primarily occurs on noisy façades and in noisy open areas; urban morphology influences the attenuation in open areas more than on façades. As ite with quieter open areas, as measured by average spatial noise levels, such as L 50 ,i sp rone to have as maller sized 'Less Noisy Area.'As ite that has greater building coverage, however, has noisy façades with higher spatial noise levels in terms of L 10 and L 20 . With an increase of the Building Plan Area Fraction (BPAF),the spatial noise levels on noisy building façades do not decrease butincrease continuously.The Complete Aspect Ratio (CAR)and the Building Frontal Area Index (BFAI) both have the greatest impact on the average spatial noise levels, such as L 60 in open areas. The reduction of noisy open areas occurs with ad ecrease in the distance between the first-rowb uildings and at rafficr oad. It has also been revealed that the noise reduction occurs with an increase of façade areas along aroad.

Humans seem to perform sound-source separation for quasi-periodic sounds, such as speech, mostly on harmonicity cues. To model this function, most machine algorithms use a pitch-based approach to group the speech parts of the spectrum. In these methods the pitch is obtained either explicitly, in autocorrelation methods, or implicitly, as in harmonic sieves. If the estimation of pitch is wrong, the grouping will fail as well. In this paper we show a method that performs harmonic grouping without first calculating the pitch. Instead a pitch estimate is associated with each grouping hypothesis. Making the grouping independent of the pitch estimate makes it more robust in noisy settings. The algorithm obtains possible harmonics by tracking energy peaks in a cochleogram. Co-occuring harmonics are compared in terms of frequency difference. Grouping hypotheses are formed by combining harmonics with similar frequency differences. Consistency checks are performed on these hypotheses and hypotheses with compatible properties are combined into harmonic complexes. Every harmonic complex is evaluated on the number of the harmonics, the number of subsequent harmonics and the presence of a harmonic at the pitch position. By using the number of subsequent harmonics octave errors are prevented. Multiple concurrent harmonic complexes can be found as long as the spectral overlap is small.

Primitive auditory scene analysis (ASA) is based on intrinsic properties of the auditory environment. Acoustic features such as continuity and proximity in time or frequency cause perceptual grouping of acoustic elements. Various grouping attributes have been translated into successful signal processing techniques that may be used in source separation. A next step beyond primitive ASA is source identification through schema-based ASA. We present a computational model for ASA that is inspired by models from cognitive research. It dynamically builds a hierarchical network of hypotheses, which is based on (learned) knowledge of the sources. Each hypothesis in the network, initiated by bottom-up evidence, represents a possible sound event. The network is updated for each new input event, which may be any sound in an unconstrained environment. The analysis of new input events is guided by knowledge of the environment and previous events. As a result of this adaptive behavior, information about the environment increases and the set of possible hypotheses decreases. With this method of continuously improving sound event identification we make a promising advance in computational ASA of complex real-world environments.