Binaural Sound Source Distance Reproduction Based on Distance Variation Function and Artificial Reverberation

2016 ◽  
pp. 101-111 ◽  
Author(s):  
Jiawang Xu ◽  
Xiaochen Wang ◽  
Maosheng Zhang ◽  
Cheng Yang ◽  
Ge Gao
Keyword(s):  
Sound Source ◽  
Source Distance ◽  
Variation Function ◽  
Binaural Sound

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

Application of Human mimetic Binaural Sound Source Direction Estimation to Task Realization for Musculoskeletal Humanoid

2021 ◽  
Vol 2021 (0) ◽  
pp. 2A1-I15
Author(s):  
Yusuke OMURA ◽  
Kento KAWAHARAZUKA ◽  
Yuya NAGAMATSU ◽  
Yuya KOGA ◽  
Manabu NISHIURA ◽  
...  
Keyword(s):  
Sound Source ◽  
Direction Estimation ◽  
Binaural Sound

Dynamic binaural sound source localization with ITD cues: Human listeners

2015 ◽  
Vol 137 (4) ◽  
pp. 2376-2376 ◽  
Author(s):  
Xuan Zhong ◽  
William Yost ◽  
Liang Sun
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Sound Source Localization ◽  
Binaural Sound

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
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