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

Near-field acoustical holography incorporating compressive sampling: Effect of measurement distance and array density

2020 ◽  
Vol 68 (6) ◽  
pp. 470-489
Author(s):  
Tongyang Shi ◽  
Weimin Thor ◽  
J. Stuart Bolton
Keyword(s):  
Sound Source ◽  
Near Field ◽  
Source Region ◽  
Sampling Rate ◽  
Sound Field ◽  
Compressive Sampling ◽  
Sound Power ◽  
L1 Norm ◽  
Measured Intensity ◽  
Acoustical Holography

To identify sound source locations and strength by using near-field acoustical holography (NAH), many microphones are generally required in order to span the source region and to ensure a sufficiently high spatial sampling rate. It is often the case that hundreds of microphones are needed, so such measurements are costly, which has limited the application of NAH in industrial settings. Recently, however, it has been shown that it is possible to accurately identify concentrated sound sources with a limited number of microphones based on compressive sampling theory. Here, the theory of the four NAH methods that were studied in the present work, that is, statistically optimized near-field acoustical holography (SONAH), wideband acoustical holography (WBH), l1-norm minimization, and a hybrid compressive sampling method, is briefly reviewed. Note that the latter three procedures incorporate elements of compressive sampling. Then, a simulation with one monopole as the sound source was conducted to illustrate some basic characteristics of these algorithms. In the experimental portion of the work, a multi-element loudspeaker was used as the sound source. A near-field intensity scan was conducted to measure both the true intensity spatial distribution and the sound power generated by the loudspeaker to provide a basis against which the values obtained from the holography reconstructions could be compared. The sound field was reconstructed by using both near- and far-field measurements, and the number of microphone measurements used to reconstruct the sound field was systematically decreased by increasing the spacing between microphones. Both in the simulation and experiment, the sound field was reconstructed by using the four NAH methods mentioned above. Then, the reconstruction results were comparedwith the measured intensity results in terms of spatial localization and sound power, and the benefits of the compressive sampling approach are illustrated.

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