Improvement of learning-based methods for localization of multiple sound sources
Author:
Publisher
Vilnius Gediminas Technical University
Link
http://dspace.vgtu.lt/bitstream/1/4309/1/S_Sakaviciaus%20disertacija.pdf
Reference103 articles.
1. Adavanne, S.; Politis, A.; Nikunen, J.; Virtanen, T. 2019a. Sound event localization and detection of overlapping sources using convolutional recurrent neural networks, IEEE Journal of Selected Topics in Signal Processing 13(1): 34-48. [see 27, 28 p.]
2. Adavanne, S.; Politis, A.; Virtanen, T. 2018. Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network, in Proceedings of the European Signal Processing Conference (EUSIPCO), Rome, Italy. [see 27 p.]
3. Adavanne, S.; Politis, A.; Virtanen, T. 2019b. Localization, detection and tracking of multiple moving sound sources with a convolutional recurrent neural network. [see 28 p.]
4. Allen, J. B.; Berkley, D. A. 1976. Image method for efficiently simulating small-room acoustics, The Journal of the Acoustical Society of America 65(4): 943-950. [see 28 p.]
5. Argentieri, S.; Danes, P.; Soueres, P. 2015. A survey on sound source localization in robotics: From binaural to array processing methods, Computer Speech and Language 34(1): 87-112. [see 1, 12, 26 p.]
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1. Multiple Sound Source Localization in Three Dimensions Using Convolutional Neural Networks and Clustering Based Post-Processing;IEEE Access;2022
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