Urban soundscape design involves creating outdoor spaces that are pleasing to the ear. One way to achieve this goal is to add or accentuate sounds that are considered to be desired by most users of the space, such that the desired sounds mask undesired sounds, or at least distract attention away from undesired sounds. In view of removing the need for a listening panel to assess the effectiveness of such soundscape measures, the interest for new models and techniques is growing. In this paper, a model of auditory attention to environmental sound is presented, which balances computational complexity and biological plausibility. Once the model is trained for a particular location, it classifies the sounds that are present in the soundscape and simulates how a typical listener would switch attention over time between different sounds. The model provides an acoustic summary, giving the soundscape designer a quick overview of the typical sounds at a particular location, and allows assessment of the perceptual effect of introducing additional sounds.

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Abstract:Previous research has shown that tranquil areas in the city, such as urban parks, are usually perceived as positive and have a restorative effect on visitors. However, visitors could experience these spaces differently depending on the meaning they assign to the concept of tranquility. To investigate how individuals' personal views on tranquility affect their perception of the sonic environment, a soundscape study was conducted in several city parks in Antwerp, Belgium. Mobile sound measurements were combined with a questionnaire survey amongst 660 park visitors. Within the survey, the participants' viewpoint on tranquility was evaluated using their agreement with a set of previously established prototypical statements, categorizing them into one out of three main tranquility viewpoint groups: people that associate tranquility with silence, those that associate it with hearing natural sounds, or those that associate it with social relationships. Next to this, the sounds that participants had heard during their visit were noted, and their perception of the overall quality of the soundscape and the degree to which it matched their expectation were assessed. Results show that the park visitors who associate tranquility with natural sounds or to silence are more often found amongst those that report hearing mechanical sounds a lot. The same groups of visitors rate the overall quality of the sonic environment of the park more often bad to very bad. These findings suggest that park visitors pay attention more to the sounds they do not expect to hear, and that the higher their expectations about the soundscape, the more critical they become in their appraisal of the soundscape.

The increasing importance attributed to soundscape quality in urban design generates a need for a system for automatic quality assessment that could be used for example in monitoring. In this work, the possibility for using machine listening techniques for this purpose is explored. The outlined approach detects the presence of particular sounds in a human-inspired way, and therefore allows to draw conclusions about how soundscapes are perceived. The system proposed in this paper consists of a partly recurrent artificial neural network modified to incorporate human attention mechanisms. The network is trained on sounds recorded in typical urban parks in the city of Antwerp, and thus becomes an auditory object creation and classification system particularly tuned to this context. The system is used to analyze a continuous sound level recording in different parks, resulting in a prediction of sounds that will most likely be noticed by a park visitor. Finally, it is shown that these indicators for noticed sounds allow to construct more powerful models for soundscape quality as reported in a survey with park visitors than indicators that are more regularly used in soundscape research.

This paper analyzes the feasibility of deep convolutional neural networks (DCNN) for accurate ultra-wideband (UWB) angle of arrival estimation that is robust against hardware imperfections. To this end, a uniform linear array with four antenna elements is leveraged and a DCNN approach is proposed and compared with traditional approaches, such as MUSIC and phase difference of arrival estimators, for different environments, number of available channel impulse responses, and polarization mismatches, in terms of absolute value of error and computational complexity. The proposed approach outperforms the traditional approaches up to 80 • error reduction at a computational complexity increase of only 10% compared to MUSIC.

-Auditory attention is an essential property of human hearing. It is responsible for the selection of information to be sent to working memory and as such to be perceived consciously, from the abundance of auditory information that is continuously entering the ears. Thus, auditory attention heavily influences human auditory perception and systems simulating human auditory scene analysis would benefit from an attention model. In this paper, a human-mimicking model of auditory attention is presented, aimed to be used in environmental sound monitoring. It relies on a Self-Organizing Map (SOM) for learning and classifying sounds. Coupled to this SOM, an excitatory-inhibitory artificial neural network (ANN), simulating the auditory cortex, is defined. The activation of these neurons is calculated based on an interplay of various excitatory and inhibitory inputs. The latter simulate auditory attention mechanisms in a humaninspired but simplified way, in order to keep the computational cost within bounds. The behavior of the model incorporating all of these mechanisms is investigated, and plausible results are obtained.

Abstract-In this paper, a human-mimicking model for sound source recognition is presented. It consists of an artificial neural network with three neuron layers (input, middle and output) that are connected by feedback connections between the output and middle layer, on top of feedforward connections from the input to middle and middle to output layers. Learning is accomplished by the model following the Hebb principle, dictating that "cells that fire together, wire together", with some important alterations, compared to standard Hebbian learning, in order to prevent the model from forgetting previously learned patterns, when learning new ones. In addition, short-term memory is introduced into the model in order to facilitate and guide learning of neuronal synapses (long-term memory). As auditory attention is an essential part of human auditory scene analysis (ASA), it is also indispensable in any computational model mimicking it, and it is shown that different auditory attention mechanism naturally emerge from the neuronal behaviour as implemented in the model described in this paper. The learning behavior of the model is further investigated in the context of an urban sonic environment, and the importance of shortterm memory in this process is demonstrated. Finally, the effectiveness of the model is evaluated by comparing model output on presented sound recordings to a human expert listeners evaluation of the same fragments.

Highlights: The acoustic summary of a place is a collection of representative sounds  Acoustic summaries of several urban and quiet area locations are constructed using an automated procedure  A validation test with local residents assesses the quality of the acoustic summaries  Local residents can easily identify the acoustic summary extracted at the location of their own dwelling  A group of sounds describes the uniqueness of a place, rather than single sounds by themselvesHighlights (for review)