13,425 research outputs found
Reflection-Aware Sound Source Localization
We present a novel, reflection-aware method for 3D sound localization in
indoor environments. Unlike prior approaches, which are mainly based on
continuous sound signals from a stationary source, our formulation is designed
to localize the position instantaneously from signals within a single frame. We
consider direct sound and indirect sound signals that reach the microphones
after reflecting off surfaces such as ceilings or walls. We then generate and
trace direct and reflected acoustic paths using inverse acoustic ray tracing
and utilize these paths with Monte Carlo localization to estimate a 3D sound
source position. We have implemented our method on a robot with a cube-shaped
microphone array and tested it against different settings with continuous and
intermittent sound signals with a stationary or a mobile source. Across
different settings, our approach can localize the sound with an average
distance error of 0.8m tested in a room of 7m by 7m area with 3m height,
including a mobile and non-line-of-sight sound source. We also reveal that the
modeling of indirect rays increases the localization accuracy by 40% compared
to only using direct acoustic rays.Comment: Submitted to ICRA 2018. The working video is available at
(https://youtu.be/TkQ36lMEC-M
Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation
To date a number of studies have shown that receptive field shapes of early
sensory neurons can be reproduced by optimizing coding efficiency of natural
stimulus ensembles. A still unresolved question is whether the efficient coding
hypothesis explains formation of neurons which explicitly represent
environmental features of different functional importance. This paper proposes
that the spatial selectivity of higher auditory neurons emerges as a direct
consequence of learning efficient codes for natural binaural sounds. Firstly,
it is demonstrated that a linear efficient coding transform - Independent
Component Analysis (ICA) trained on spectrograms of naturalistic simulated
binaural sounds extracts spatial information present in the signal. A simple
hierarchical ICA extension allowing for decoding of sound position is proposed.
Furthermore, it is shown that units revealing spatial selectivity can be
learned from a binaural recording of a natural auditory scene. In both cases a
relatively small subpopulation of learned spectrogram features suffices to
perform accurate sound localization. Representation of the auditory space is
therefore learned in a purely unsupervised way by maximizing the coding
efficiency and without any task-specific constraints. This results imply that
efficient coding is a useful strategy for learning structures which allow for
making behaviorally vital inferences about the environment.Comment: 22 pages, 9 figure
Scan and paint: theory and practice of a sound field visualization method
Sound visualization techniques have played a key role in the development of acoustics throughout history. The development of measurement apparatus and techniques for displaying sound and vibration phenomena has provided excellent tools for building understanding about specific problems. Traditional methods, such as step-by-step measurements or simultaneous multichannel systems, have a strong tradeoff between time requirements, flexibility, and cost. However, if the sound field can be assumed time stationary, scanning methods allow us to assess variations across space with a single transducer, as long as the position of the sensor is known. The proposed technique, Scan and Paint, is based on the acquisition of sound pressure and particle velocity by manually moving a P-U probe (pressure-particle velocity sensors) across a sound field whilst filming the event with a camera. The sensor position is extracted by applying automatic color tracking to each frame of the recorded video. It is then possible to visualize sound variations across the space in terms of sound pressure, particle velocity, or acoustic intensity. In this paper, not only the theoretical foundations of the method, but also its practical applications are explored such as scanning transfer path analysis, source radiation characterization, operational deflection shapes, virtual phased arrays, material characterization, and acoustic intensity vector field mapping
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Relationships between human auditory cortical structure and function
The human auditory cortex comprises multiple areas, largely distributed across the supratemporal plane, but the precise number and configuration of auditory areas and their functional significance have not yet been clearly established. In this paper, we discuss recent research concerning architectonic and functional organisation within the human auditory cortex, as well as architectonic and neurophysiological studies in non-human species, which can provide a broad conceptual framework for interpreting functional specialisation in humans. We review the pattern in human auditory cortex of the functional responses to various acoustic cues, such as frequency, pitch, sound level, temporal variation, motion and spatial location, and we discuss their correspondence to what is known about the organisation of the auditory cortex in other primates. There is some neuroimaging evidence of multiple tonotopically organised fields in humans and of functional specialisations of the fields in the processing of different sound features. It is thought that the primary area, on Heschl's gyrus, may have a larger involvement in processing basic sound features, such as frequency and level, and that posterior non-primary areas on the planum temporale may play a larger role in processing more spectrotemporally complex sounds. Ways in which current knowledge of auditory cortical organisation and different data analysis approaches may benefit future functional neuroimaging studies which seek to link auditory cortical structure and function are discussed
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