Damiano Oldoni

A computational model of auditory attention for use in soundscape research

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.

Context-dependent environmental sound monitoring using SOM coupled with LEGION

Environmental sound measurement networks are increasingly applied for monitoring noise pollution in an urban context. Intelligent measurement nodes offer the opportunity to perform advanced analysis of environmental sound, but tradeoffs between cost and functionality still have to be made. When using a tiered architecture, local nodes with limited computing capabilities can be used to detect sound events of potential interest, which are then further analyzed by more powerful nodes. This paper presents a human-mimicking model for detecting rare and conspicuous sound events. Features encoding spectro-temporal irregularities are extracted from the sound, and a Self-Organizing Map (SOM) is used to identify co-occurring features, which most likely belong to a single sound object. Extensive training allows this map to be tuned to the typical sounds that are heard at the microphone location. A Locally Excitatory Globally Inhibitory Oscillator Network (LEGION) is used to group units of the SOM in order to construct distinct sound objects.

A biologically inspired recurrent neural network for sound source recognition incorporating auditory attention

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 short-term 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.

Attention-driven auditory stream segregation using a SOM coupled with an excitatory-inhibitory ANN

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 human-inspired 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.

The role of paying attention to sounds in soundscape perception

It has been stated frequently that the soundscape as perceived and appraised by the user of a space, extends beyond the physical stimulus. We argue that, when introducing to human-factor in analyzing a sonic environment, the sounds that people hear play an important role. This holds in particular for rather quiet and infrequent disturbance of park soundscapes. Auditory attention mechanisms are essential in the process. Attention can be drawn by saliency elements such as changes in time and frequency, but it can also be outward oriented and voluntary. These mechanisms could explain the special role of natural sounds in distracting attention from mechanical background hum in a park environment. These theoretical concepts have now been implemented in measuring equipment that allows estimating how often particular sounds will be heard by a human listener. The methodology includes biologically inspired feature extraction, learning based on co-occurrence of features and saliency, attention focusing, and inhibit...

Characterizing the soundscape of tranquil urban spaces

Tranquil spaces provide restorative environments for urban residents and visitors and are therefore essential for health and quality of life. Tranquil spaces may be characterized through a combination of acoustical criteria, such as relatively low (percentile) sound levels and the relative absence of non-fitting sounds, and non-acoustical criteria, such as the presence of natural elements within the visual scene. Public urban parks and courtyards as well as private urban backyards are typically considered to be the most tranquil spots within a city. Current state-of-the-art in distributed measurement technology allows for long-term sound monitoring at these places. In this paper, the soundscape at a number of urban parks and backyards in the cities of Ghent and Antwerp is investigated through a detailed analysis of sound measurements performed over an extended period of time. An analysis of percentile sound levels, noise events and indicators for temporal and spectral structure is presented, and novel com...

Acoustic summary as a tool for soundscape analysis and design.

The soundscape approach to designing sonic environments recognizes the importance of the sounds that can be heard by the attentive listener. These sounds not only contribute to the affective component of appraisal of the sonic environment but also carry the cultural identity of the neighborhood. For the purpose of discussion and communication in the soundscape design process, it is suggested to use a compilation of typical sounds amended with a sample of unusual sounds. To create such an acoustic summary automatically, a clustering system based on self‐organizing maps (also called Kohonen networks) using well‐chosen acoustical features is proposed. In addition, an oscillating neural network groups the sound into auditory streams with well defined duration. The proposed computational system continuously learns to identify the sounds that surround it and allows retrieving prototypical sounds for all identified clusters of sounds. Since the system is trained for a specific environment, the acoustic summary t...