Virtual sound source distance evaluation in acoustically and visually incongruent contexts

2021 ◽  
Vol 150 (4) ◽  
pp. A140-A141
Author(s):  
Vincent Martin ◽  
Olivier Warusfel ◽  
Isabelle Viaud-Delmon
Keyword(s):  
Sound Source ◽  
Source Distance

scholarly journals Near-Field Discrimination of Sound Source Distance in the Rabbit

2015 ◽  
Vol 16 (2) ◽  
pp. 255-262 ◽  
Author(s):  
Shigeyuki Kuwada ◽  
Duck O. Kim ◽  
Kelly-Jo Koch ◽  
Kristina S. Abrams ◽  
Fabio Idrobo ◽  
...  
Keyword(s):  
Sound Source ◽  
Near Field ◽  
Source Distance

scholarly journals Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 172
Author(s):  
Mariam Yiwere ◽  
Eun Joo Rhee
Keyword(s):  
Image Classification ◽  
Sound Source ◽  
Orientation Angle ◽  
Audio Signal ◽  
Distance Estimation ◽  
Classification Problem ◽  
Audio Signals ◽  
Time Frequency ◽  
Proposed Model ◽  
Source Distance

This paper presents a sound source distance estimation (SSDE) method using a convolutional recurrent neural network (CRNN). We approach the sound source distance estimation task as an image classification problem, and we aim to classify a given audio signal into one of three predefined distance classes—one meter, two meters, and three meters—irrespective of its orientation angle. For the purpose of training, we create a dataset by recording audio signals at the three different distances and three angles in different rooms. The CRNN is trained using time-frequency representations of the audio signals. Specifically, we transform the audio signals into log-scaled mel spectrograms, allowing the convolutional layers to extract the appropriate features required for the classification. When trained and tested with combined datasets from all rooms, the proposed model exhibits high classification accuracies; however, training and testing the model in separate rooms results in lower accuracies, indicating that further study is required to improve the method’s generalization ability. Our experimental results demonstrate that it is possible to estimate sound source distances in known environments by classification using the log-scaled mel spectrogram.

Loudness constancy with varying sound source distance

10.1038/82931 ◽  
2001 ◽  
Vol 4 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Pavel Zahorik ◽  
Frederic L. Wightman
Keyword(s):  
Sound Source ◽  
Source Distance

scholarly journals Loudness judgements are not necessarily affected by visual cues to sound source distance

2017 ◽  
Author(s):  
sol libesman ◽  
Thomas Whitford ◽  
Damien Mannion
Keyword(s):  
Sound Source ◽  
Visual Cues ◽  
Apparent Distance ◽  
Sound Level ◽  
Acoustic Energy ◽  
Visual Environment ◽  
Potential Contribution ◽  
Subjective Loudness ◽  
Source Distance ◽  
Source Level

The level of the auditory signals at the ear depends both on the capacity of the sound source to produce acoustic energy and on the distance of the source from the listener. Loudness constancy requires that our perception of sound level, loudness, corresponds to the source level by remaining invariant to the confounding effects of distance. Here, we assessed the evidence for a potential contribution of vision, via the disambiguation of sound source distance, to loudness constancy. We presented participants with a visual environment, on a computer monitor, which contained a visible loudspeaker at a particular distance and was accompanied by the auditory delivery, via headphones, of an anechoic sound of a particular aural level. We measured the point of subjective loudness equality for sounds associated with loudspeakers at different visually-depicted distances. We report strong evidence that such loudness judgements were closely aligned with the aural level, rather than being affected by the apparent distance of the sound source conveyed visually. Similar results were obtained across variations in sound and environment characteristics. We conclude that the loudness of anechoic sounds are not necessarily affected by indications of the sound source distance as established via vision.

Sound source distance estimation using a small-size microphone array

2012 ◽  
Vol 131 (4) ◽  
pp. 3499-3499
Author(s):  
Satoshi Esaki ◽  
Takanori Nishino ◽  
Kazuya Takeda
Keyword(s):  
Sound Source ◽  
Microphone Array ◽  
Distance Estimation ◽  
Source Distance

scholarly journals Assessment of Sound Source Tracking Using Multiple Drones Equipped with Multiple Microphone Arrays

2021 ◽  
Vol 18 (17) ◽  
pp. 9039
Author(s):  
Taiki Yamada ◽  
Katsutoshi Itoyama ◽  
Kenji Nishida ◽  
Kazuhiro Nakadai
Keyword(s):  
Numerical Simulation ◽  
Performance Assessment ◽  
Sound Source ◽  
Microphone Arrays ◽  
Source Tracking ◽  
Signal To Noise ◽  
Sound Sources ◽  
Tracking Method ◽  
Low Snr ◽  
Source Distance

Drone audition techniques are helpful for listening to target sound sources from the sky, which can be used for human searching tasks in disaster sites. Among many techniques required for drone audition, sound source tracking is an essential technique, and thus several tracking methods have been proposed. Authors have also proposed a sound source tracking method that utilizes multiple microphone arrays to obtain the likelihood distribution of the sound source locations. These methods have been demonstrated in benchmark experiments. However, the performance against various sound sources with different distances and signal-to-noise ratios (SNRs) has been less evaluated. Since drone audition often needs to listen to distant sound sources and the input acoustic signal generally has a low SNR due to drone noise, making a performance assessment against source distance and SNR is essential. Therefore, this paper presents a concrete evaluation of sound source tracking methods using numerical simulation, focusing on various source distances and SNRs. The simulated results captured how the tracking performance will change when the sound source distance and SNR change. The proposed approach based on location distribution estimation tended to be more robust against distance increase, while existing approaches based on directional estimation tended to be more robust against decreasing SNR.

Extracting Sound-Source-Distance Information from Binaural Signals

2013 ◽  
pp. 171-199 ◽  
Author(s):  
E. Georganti ◽  
T. May ◽  
S. van de Par ◽  
J. Mourjopoulos
Keyword(s):  
Sound Source ◽  
Distance Information ◽  
Source Distance
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