CNN-based Robust Sound Source Localization with SRP-PHAT for the Extreme Edge
Authors: 
Jun Yin, Marian VerhelstAuthors Info & Claims
Jun Yin, Marian VerhelstAuthors Info & ClaimsArticle No.: 55, Pages 1 - 27
Abstract
Robust sound source localization for environments with noise and reverberation are increasingly exploiting deep neural networks fed with various acoustic features. Yet, state-of-the-art research mainly focuses on optimizing algorithmic accuracy, resulting in huge models preventing edge-device deployment. The edge, however, urges for real-time low-footprint acoustic reasoning for applications such as hearing aids and robot interactions. Hence, we set off from a robust CNN-based model using SRP-PHAT features, Cross3D [16], to pursue an efficient yet compact model architecture for the extreme edge. For both the SRP feature representation and neural network, we propose respectively our scalable LC-SRP-Edge and Cross3D-Edge algorithms which are optimized towards lower hardware overhead. LC-SRP-Edge halves the complexity and on-chip memory overhead for the sinc interpolation compared to the original LC-SRP [19]. Over multiple SRP resolution cases, Cross3D-Edge saves 10.32%~73.71% computational complexity and 59.77%~94.66% neural network weights against the Cross3D baseline. In terms of the accuracy-efficiency tradeoff, the most balanced version (EM) requires only 127.1 MFLOPS computation, 3.71 MByte/s bandwidth, and 0.821 MByte on-chip memory in total, while still retaining competitiveness in state-of-the-art accuracy comparisons. It achieves 8.59 ms/frame end-to-end latency on a Rasberry Pi 4B, which is 7.26× faster than the corresponding baseline.
References
[1]
Sharath Adavanne, Archontis Politis, Joonas Nikunen, and Tuomas Virtanen. 2018. Sound event localization and detection of overlapping sources using convolutional recurrent neural networks. IEEE Journal of Selected Topics in Signal Processing 13, 1 (2018), 34–48.
[2]
Sharath Adavanne, Archontis Politis, and Tuomas Virtanen. 2018. Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network. In Proceedings of the 2018 26th European Signal Processing Conference. IEEE, 1462–1466.
[3]
Sharath Adavanne, Archontis Politis, and Tuomas Virtanen. 2019. Localization, detection and tracking of multiple moving sound sources with a convolutional recurrent neural network. In Workshop on Detection and Classification of Acoustic Scenes and Events.
[4]
Sharath Adavanne, Archontis Politis, and Tuomas Virtanen. 2019. A multi-room reverberant dataset for sound event localization and detection. In Workshop on Detection and Classification of Acoustic Scenes and Events.
[5]
Jont B. Allen and David A. Berkley. 1979. Image method for efficiently simulating small-room acoustics. The Journal of the Acoustical Society of America 65, 4 (1979), 943–950.
[6]
Yin Cao, Turab Iqbal, Qiuqiang Kong, Fengyan An, Wenwu Wang, and Mark D. Plumbley. 2021. An improved event-independent network for polyphonic sound event localization and detection. In Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 885–889.
[7]
Soumitro Chakrabarty and Emanuël A. P. Habets. 2017. Broadband DOA estimation using convolutional neural networks trained with noise signals. In Proceedings of the 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. IEEE, 136–140.
[8]
Soumitro Chakrabarty and Emanuël A. P. Habets. 2017. Multi-speaker localization using convolutional neural network trained with noise. arXiv:1712.04276 [cs.SD].
[9]
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Q. Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In USENIX Symposium on Operating Systems Design and Implementation.
[10]
Paolo Chiariotti, Milena Martarelli, and Paolo Castellini. 2019. Acoustic beamforming for noise source localization–Reviews, methodology and applications. Mechanical Systems and Signal Processing 120 (2019), 422–448. https://www.sciencedirect.com/science/article/abs/pii/S088832701830637X.
[11]
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Conference on Empirical Methods in Natural Language Processing.
[12]
François Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1251–1258.
[13]
Luca Comanducci, Federico Borra, Paolo Bestagini, Fabio Antonacci, Stefano Tubaro, and Augusto Sarti. 2020. Source localization using distributed microphones in reverberant environments based on deep learning and ray space transform. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020), 2238–2251. https://ieeexplore.ieee.org/abstract/document/9146703.
[14]
Jorge Dávila-Chacón, Jindong Liu, and Stefan Wermter. 2018. Enhanced robot speech recognition using biomimetic binaural sound source localization. IEEE Transactions on Neural Networks and Learning Systems 30, 1 (2018), 138–150.
[15]
David Diaz-Guerra. 2020. Cross3D Codebase. Retrieved from https://github.com/DavidDiazGuerra/Cross3D.
[16]
David Diaz-Guerra, Antonio Miguel, and Jose R. Beltran. 2020. Robust sound source tracking using SRP-PHAT and 3D convolutional neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2020), 300–311. https://ieeexplore.ieee.org/abstract/document/9268154.
[17]
David Diaz-Guerra, Antonio Miguel, and Jose R. Beltran. 2021. gpuRIR: A python library for room impulse response simulation with GPU acceleration. Multimedia Tools and Applications 80, 4 (2021), 5653–5671.
[18]
Joseph H. DiBiase, Harvey F. Silverman, and Michael S. Brandstein. 2001. Robust localization in reverberant rooms. In Proceedings of the Microphone Arrays. Springer, 157–180.
[19]
Thomas Dietzen, Enzo De Sena, and Toon van Waterschoot. 2020. Low-complexity steered response power mapping based on Nyquist-Shannon sampling. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA’21), 206–210.
[20]
Jacek P. Dmochowski, Jacob Benesty, and Sofiene Affes. 2007. A generalized steered response power method for computationally viable source localization. IEEE Transactions on Audio, Speech, and Language Processing 15, 8 (2007), 2510–2526.
[21]
Hoang Do, Harvey F. Silverman, and Ying Yu. 2007. A real-time SRP-PHAT source location implementation using stochastic region contraction (SRC) on a large-aperture microphone array. In Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07. Vol. 1. IEEE, I–121.
[22]
Christine Evers, Heinrich W. Löllmann, Heinrich Mellmann, Alexander Schmidt, Hendrik Barfuss, Patrick A. Naylor, and Walter Kellermann. 2020. The LOCATA challenge: Acoustic source localization and tracking. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020), 1620–1643. https://ieeexplore.ieee.org/abstract/document/9079214.
[23]
Pierre-Amaury Grumiaux, Srdan Kitic, Laurent Girin, and Alexandre Guérin. 2021. Improved feature extraction for CRNN-based multiple sound source localization. In 29th European Signal Processing Conference (EUSIPCO’21). 231–235.
[24]
Pierre-Amaury Grumiaux, Srđan Kitić, Laurent Girin, and Alexandre Guérin. 2021. A survey of sound source localization with deep learning methods. The Journal of the Acoustical Society of America 152, 1 (2021), 107.
[25]
Karim Guirguis, Christoph Schorn, Andre Guntoro, Sherif Abdulatif, and Bin Yang. 2021. SELD-TCN: Sound event localization & detection via temporal convolutional networks. In Proceedings of the 2020 28th European Signal Processing Conference. IEEE, 16–20.
[26]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision. 1026–1034.
[27]
Toni Hirvonen. 2015. Classification of spatial audio location and content using convolutional neural networks. In Proceedings of the Audio Engineering Society Convention 138. Audio Engineering Society.
[28]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.
[29]
Kotaro Hoshiba, Kai Washizaki, Mizuho Wakabayashi, Takahiro Ishiki, Makoto Kumon, Yoshiaki Bando, Daniel Gabriel, Kazuhiro Nakadai, and Hiroshi G. Okuno. 2017. Design of UAV-embedded microphone array system for sound source localization in outdoor environments. Sensors 17, 11 (2017), 2535.
[30]
Yankun Huang, Xihong Wu, and Tianshu Qu. 2020. A time-domain unsupervised learning based sound source localization method. In Proceedings of the 2020 IEEE 3rd International Conference on Information Communication and Signal Processing. IEEE, 26–32.
[31]
Daniel P. Jarrett, Emanuël A. P. Habets, and Patrick A. Naylor. 2017. Theory and Applications of Spherical Microphone Array Processing. Vol. 9. Springer.
[32]
Wen Jie Jee, R. Mars, P. Pratik, S. Nagisetty, and C. S. Lim. 2019. Sound Event Localization and Detection using Convolutional Recurrent Neural Network. Technical Report. DCASE2019 Challenge, Tech. Rep.
[33]
Sławomir Kapka and Mateusz Lewandowski. 2019. Sound source detection, localization and classification using consecutive ensemble of CRNN models. ArXiv abs/1908.00766 (2019).
[34]
Youngwook Kim and Hao Ling. 2011. Direction of arrival estimation of humans with a small sensor array using an artificial neural network. Progress In Electromagnetics Research B 27 (2011), 127–149.
[35]
Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014).
[36]
Charles Knapp and Glifford Carter. 1976. The generalized correlation method for estimation of time delay. IEEE Transactions on Acoustics, Speech, and Signal Processing 24, 4 (1976), 320–327.
[37]
Qiuqiang Kong, Yin Cao, Turab Iqbal, Yong Xu, Wenwu Wang, and Mark D. Plumbley. 2019. Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems. arXiv:1904.03476 [cs.SD].
[38]
Tribikram Kundu. 2014. Acoustic source localization. Ultrasonics 54, 1 (2014), 25–38.
[39]
Tribikram Kundu, Hayato Nakatani, and Nobuo Takeda. 2012. Acoustic source localization in anisotropic plates. Ultrasonics 52, 6 (2012), 740–746.
[40]
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, and Don Joven Agravante. 2019. Learning multiple sound source 2d localization. In Proceedings of the 2019 IEEE 21st International Workshop on Multimedia Signal Processing. IEEE, 1–6.
[41]
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.
[42]
Qinglong Li, Xueliang Zhang, and Hao Li. 2018. Online direction of arrival estimation based on deep learning. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2616–2620.
[43]
Markus V. S. Lima, Wallace A. Martins, Leonardo O. Nunes, Luiz W. P. Biscainho, Tadeu N. Ferreira, Mauricio V. M. Costa, and Bowon Lee. 2015. A volumetric SRP with refinement step for sound source localization. IEEE Signal Processing Letters 22, 8 (2015), 1098–1102.
[44]
Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. arXiv:1711.05101 [cs.LG].
[45]
Robert J II Marks. 2012. Introduction to Shannon Sampling and Interpolation Theory. Springer Science & Business Media.
[46]
Brian McFee, Colin Raffel, Dawen Liang, Daniel P. W. Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. 2015. librosa: Audio and music signal analysis in python. In Proceedings of the 14th Python in Science Conference. Vol. 8. Citeseer, 18–25.
[47]
Vicente Peruffo Minotto, Claudio Rosito Jung, Luiz Gonzaga da Silveira Jr, and Bowon Lee. 2013. GPU-based approaches for real-time sound source localization using the SRP-PHAT algorithm. The International Journal of High Performance Computing Applications 27, 3 (2013), 291–306.
[48]
Javier Naranjo-Alcazar, Sergi Perez-Castanos, Jose Ferrandis, Pedro Zuccarello, and Maximo Cobos. 2021. Sound Event Localization and Detection using Squeeze-Excitation Residual CNNs. arXiv:2006.14436 [cs.SD].
[49]
Haiqiang Niu, Emma Reeves, and Peter Gerstoft. 2017. Source localization in an ocean waveguide using supervised machine learning. The Journal of the Acoustical Society of America 142, 3 (2017), 1176–1188.
[50]
Kyoungjin Noh, C. Jeong-Hwan, J. Dongyeop, and C. Joon-Hyuk. 2019. Three-stage approach for sound event localization and detection. Tech. Report of Detection and Classification of Acoustic Scenes and Events 2019 (DCASE) Challange (2019). https://www.semanticscholar.org/paper/THREE-STAGE-APPROACH-FOR-SOUND-EVENT-LOCALIZATION-Noh-Choi/2e0962d0fc80a5b069a09716b35e4fa1ecdb97b1.
[51]
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. 2015. Librispeech: An asr corpus based on public domain audio books. In Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 5206–5210.
[52]
Lauréline Perotin, Romain Serizel, Emmanuel Vincent, and Alexandre Guérin. 2018. CRNN-based joint azimuth and elevation localization with the Ambisonics intensity vector. In Proceedings of the 2018 16th International Workshop on Acoustic Signal Enhancement. IEEE, 241–245.
[53]
Pasi Pertilä and Emre Cakir. 2017. Robust direction estimation with convolutional neural networks based steered response power. In Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 6125–6129.
[54]
Archontis Politis, Sharath Adavanne, and Tuomas Virtanen. 2020. A dataset of reverberant spatial sound scenes with moving sources for sound event localization and detection. arXiv:2006.01919 [eess.AS].
[55]
Nils Poschadel, Robert Hupke, Stephan Preihs, and Jürgen Peissig. 2021. Direction of arrival estimation of noisy speech using convolutional recurrent neural networks with higher-order ambisonics signals. In 29th European Signal Processing Conference (EUSIPCO’21), 211–215.
[56]
Hadrien Pujol, Eric Bavu, and Alexandre Garcia. 2019. Source localization in reverberant rooms using Deep Learning and microphone arrays. In Proceedings of the 23rd International Congress on Acoustics.
[57]
Hadrien Pujol, Eric Bavu, and Alexandre Garcia. 2021. BeamLearning: An end-to-end Deep Learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data. The Journal of the Acoustical Society of America 149, 6 (2021), 4248–4263.
[58]
Caleb Rascon and Ivan Meza. 2017. Localization of sound sources in robotics: A review. Robotics and Autonomous Systems 96 (2017), 184–210. https://www.sciencedirect.com/science/article/pii/S0921889016304742.
[59]
Scott Rickard and Ozgiir Yilmaz. 2002. On the approximate W-disjoint orthogonality of speech. In Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol. 1. IEEE, I–529.
[60]
Reinhild Roden, Niko Moritz, Stephan Gerlach, Stefan Weinzierl, and Stefan Goetze. 2015. On Sound Source Localization of Speech Signals using Deep Neural Networks. https://www.semanticscholar.org/paper/On-sound-source-localization-of-speech-signals-deep-Roden-Moritz/cbbcd9214f1d25aaf4cae3cddbf0d9712056e837.
[61]
Richard Roy and Thomas Kailath. 1989. ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Transactions on Acoustics, Speech, and Signal Processing 37, 7 (1989), 984–995.
[62]
Daniele Salvati, Carlo Drioli, and Gian Luca Foresti. 2018. Exploiting CNNs for improving acoustic source localization in noisy and reverberant conditions. IEEE Transactions on Emerging Topics in Computational Intelligence 2, 2 (2018), 103–116.
[63]
Hiroshi Sawada, Ryo Mukai, and Shoji Makino. 2003. Direction of arrival estimation for multiple source signals using independent component analysis. In Proceedings of the 7th International Symposium on Signal Processing and Its Applications. Vol. 2. IEEE, 411–414.
[64]
Ralph Schmidt. 1986. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation 34, 3 (1986), 276–280.
[65]
Christopher Schymura, Benedikt T. Bönninghoff, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Tomohiro Nakatani, Shoko Araki, and Dorothea Kolossa. 2021. PILOT: Introducing transformers for probabilistic sound event localization. In Interspeech.
[66]
Kazuki Shimada, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, and Yuki Mitsufuji. 2021. Accdoa: Activity-coupled cartesian direction of arrival representation for sound event localization and detection. In Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 915–919.
[67]
Kazuki Shimada, Naoya Takahashi, Shusuke Takahashi, and Yuki Mitsufuji. 2020. Sound event localization and detection using activity-coupled Cartesian DOA vector and RD3Net. arXiv:2006.12014 [eess.AS].
[68]
Sunit Sivasankaran, Emmanuel Vincent, and Dominique Fohr. 2018. Keyword-based speaker localization: Localizing a target speaker in a multi-speaker environment. In Proceedings of the Interspeech 2018-19th Annual Conference of the International Speech Communication Association.
[69]
Aswin Shanmugam Subramanian, Chao Weng, Shinji Watanabe, Meng Yu, and Dong Yu. 2021. Deep learning based multi-source localization with source splitting and its effectiveness in multi-talker speech recognition. Comput. Speech Lang. 75 (2021), 101360.
[70]
Dmitry Suvorov, Ge Dong, and Roman Zhukov. 2018. Deep residual network for sound source localization in the time domain. arXiv:1808.06429 [cs.SD].
[71]
Sakari Tervo and Tapio Lokki. 2008. Interpolation methods for the SRP-PHAT algorithm. In Proceedings of the 11th International Workshop on Acoustic Echo and Noise Control.14–17.
[72]
Etienne Thuillier, Hannes Gamper, and Ivan J. Tashev. 2018. Spatial audio feature discovery with convolutional neural networks. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 6797–6801.
[73]
Vlad M. Trifa, Ansgar Koene, Jan Morén, and Gordon Cheng. 2007. Real-time acoustic source localization in noisy environments for human-robot multimodal interaction. In Proceedings of the RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 393–398.
[74]
Hirofumi Tsuzuki, Mauricio Kugler, Susumu Kuroyanagi, and Akira Iwata. 2013. An approach for sound source localization by complex-valued neural network. IEICE Transactions on Information and Systems 96, 10 (2013), 2257–2265.
[75]
Tim Van den Bogaert, Evelyne Carette, and Jan Wouters. 2011. Sound source localization using hearing aids with microphones placed behind-the-ear, in-the-canal, and in-the-pinna. International Journal of Audiology 50, 3 (2011), 164–176.
[76]
Vishnuvardhan Varanasi, Harshit Gupta, and Rajesh M. Hegde. 2020. A deep learning framework for robust DOA estimation using spherical harmonic decomposition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020), 1248–1259. https://ieeexplore.ieee.org/abstract/document/9056464.
[77]
Reza Varzandeh, Kamil Adiloğlu, Simon Doclo, and Volker Hohmann. 2020. Exploiting periodicity features for joint detection and DOA estimation of speech sources using convolutional neural networks. In Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 566–570.
[78]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems. 5998–6008.
[79]
Paolo Vecchiotti, Emanuele Principi, Stefano Squartini, and Francesco Piazza. 2018. Deep neural networks for joint voice activity detection and speaker localization. In Proceedings of the 2018 26th European Signal Processing Conference. IEEE, 1567–1571.
[80]
Juan Manuel Vera-Diaz, Daniel Pizarro, and Javier Macias-Guarasa. 2018. Towards end-to-end acoustic localization using deep learning: From audio signals to source position coordinates. Sensors 18, 10 (2018), 3418.
[81]
Emmanuel Vincent, Tuomas Virtanen, and Sharon Gannot. 2018. Audio Source Separation and Speech Enhancement. John Wiley & Sons.
[82]
Qing Wang, Jun Du, Hua-Xin Wu, Jia Pan, Feng Ma, and Chin-Hui Lee. 2023. A four-stage data augmentation approach to ResNet-Conformer based acoustic modeling for sound event localization and detection. arXiv:2101.02919 [cs.SD].
[83]
Qing Wang, Huaxin Wu, Zijun Jing, Feng Ma, Yi Fang, Yuxuan Wang, Tairan Chen, Jia Pan, Jun Du, and Chin-Hui Lee. 2020. The USTC-IFLYTEK system for sound event localization and detection of DCASE2020 challenge. Tech. Rep., DCASE2020 Challenge (2020). https://www.semanticscholar.org/paper/THE-USTC-IFLYTEK-SYSTEM-FOR-SOUND-EVENT-AND-OF-Wang-Wu/735990cac7c3791725ac4c846ac61a603409d66b.
[84]
Zhong-Qiu Wang, Xueliang Zhang, and DeLiang Wang. 2018. Robust speaker localization guided by deep learning-based time-frequency masking. IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, 1 (2018), 178–188.
[85]
Samuel Williams, Andrew Waterman, and David Patterson. 2009. Roofline: An insightful visual performance model for multicore architectures. Communications of the ACM 52, 4 (2009), 65–76.
[86]
Yifan Wu, Roshan Ayyalasomayajula, Michael J. Bianco, Dinesh Bharadia, and Peter Gerstoft. 2021. SSLIDE: Sound source localization for indoors based on deep learning. In Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 4680–4684.
[87]
Angeliki Xenaki, Jesper Bünsow Boldt, and Mads Græsbøll Christensen. 2018. Sound source localization and speech enhancement with sparse Bayesian learning beamforming. The Journal of the Acoustical Society of America 143, 6 (2018), 3912–3921.
[88]
Xiong Xiao, Shengkui Zhao, Xionghu Zhong, Douglas L. Jones, Eng Siong Chng, and Haizhou Li. 2015. A learning-based approach to direction of arrival estimation in noisy and reverberant environments. In Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2814–2818.
[89]
Bin Xu, Guodong Sun, Ran Yu, and Zheng Yang. 2012. High-accuracy TDOA-based localization without time synchronization. IEEE Transactions on Parallel and Distributed Systems 24, 8 (2012), 1567–1576.
[90]
Nelson Yalta, Kazuhiro Nakadai, and Tetsuya Ogata. 2017. Sound source localization using deep learning models. Journal of Robotics and Mechatronics 29, 1 (2017), 37–48.
[91]
Masahiro Yasuda, Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, and Keisuke Imoto. 2020. Sound event localization based on sound intensity vector refined by DNN-based denoising and source separation. In Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 651–655.
[92]
Karim Youssef, Sylvain Argentieri, and Jean-Luc Zarader. 2013. A learning-based approach to robust binaural sound localization. In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2927–2932.
[93]
Wangyou Zhang, Ying Zhou, and Yanmin Qian. 2019. Robust DOA estimation based on convolutional neural network and time-frequency masking. In Proceedings of the INTERSPEECH. 2703–2707.
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- CNN-based Robust Sound Source Localization with SRP-PHAT for the Extreme Edge
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Published: 19 April 2023
Online AM: 06 March 2023
Accepted: 12 February 2023
Revised: 18 November 2022
Received: 17 February 2022
Published in TECS Volume 22, Issue 3
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