We present a technique for simulating accurate physically modelled acoustics within an outdoor urban environment and a tool that presents the acoustics alongside a visually rendered counterpart. Acoustic modelling is achieved by using a mixture of simulating ray-traced specular sound wave reflections and applying radiosity to simulate diffuse reflections. Sound rendering is applied to the energ...

A powerful approach for handling uncertainty in observations is to modify the statistical model of the data to appropriately reflect this uncertainty. For the task of noise robust speech recognition, this requires modifying an underlying ”clean” acoustic model to be representative of speech in a particular target acoustic environment. This chapter describes the underlying concepts of model-base...

There exists a number of cepstral de-noising algorithms which perform quite well when trained and tested under similar acoustic environments, but degrade quickly under mismatched conditions. We present two key results that make these algorithms practical in real noise environments, with the ability to adapt to different acoustic environments over time. First, we show that it is possible to leve...

The scientific objective of this research is to understand acoustic wave propagation in a dynamic environment in two frequency bands: Low (50 Hz to 500 Hz) and Mid-to-High (500 Hz to 50 kHz). The goal for the low frequency band is to assess the effects of internal waves on acoustic wave propagation, with an emphasis on the mechanisms that cause significant temporal and spatial acoustic intensit...

The scientific objective of this research is to understand acoustic wave propagation in a dynamic environment in two frequency bands: Low (50 Hz to 500 Hz) and Mid-to-High (500 Hz to 50 kHz). The goal for the low frequency band is to assess the effects of internal waves on acoustic wave propagation, with an emphasis on the mechanisms that cause significant temporal and spatial acoustic intensit...

The role and application of the concept of the soundscape, vis-à-vis that of environmental noise management, needs elaboration. In noise control, sound is a waste product, managed to reduce the immision of sounds that cause human discomfort. The soundscape approach, by contrast, considers the acoustic environment as a resource, focussing on sounds people want, or prefer. Quiet is not a core req...

The perception of the environment using opto-acoustic scene analysis techniques [1] is an extremely important and challenging task for humanoid robots. Thereby, the acquisition of visual and acoustic information provides the basis of the perception and enables the robot to interact with humans and to assist in everyday tasks. Another step is the exploration that allows the robot to actively gat...

We propose a novel deep neural network architecture for speech recognition that explicitly employs knowledge of the background environmental noise within a deep neural network acoustic model. A deep neural network is used to predict the acoustic environment in which the system in being used. The discriminative embedding generated at the bottleneck layer of this network is then concatenated with...

Scientific research on how people perceive or experience and/or understand the acoustic environment as a whole (i.e., soundscape) is still in development. In order to predict how people would perceive an acoustic environment, it is central to identify its underlying acoustic properties. This was the purpose of the present study. Three successive experiments were conducted. With the aid of 30 un...

The role and application of the concepts of soundscape planning, vis-à-vis those of environmental noise management, need elaboration. In noise control, sound is a waste product managed to reduce the immision of sounds that cause human discomfort. The soundscape approach, by contrast, considers the acoustic environment as a resource, focussing on sounds people want, or prefer. Quiet is not a cor...