{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:38:07Z","timestamp":1774021087964,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T00:00:00Z","timestamp":1604534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T00:00:00Z","timestamp":1604534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J AUDIO SPEECH MUSIC PROC."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n<jats:p>Drone-embedded sound source localization (SSL) has interesting application perspective in challenging search and rescue scenarios due to bad lighting conditions or occlusions. However, the problem gets complicated by severe drone ego-noise that may result in negative signal-to-noise ratios in the recorded microphone signals. In this paper, we present our work on drone-embedded SSL using recordings from an 8-channel cube-shaped microphone array embedded in an unmanned aerial vehicle (UAV). We use angular spectrum-based TDOA (time difference of arrival) estimation methods such as generalized cross-correlation phase-transform (GCC-PHAT), minimum-variance-distortion-less-response (MVDR) as baseline, which are state-of-the-art techniques for SSL. Though we improve the baseline method by reducing ego-noise using speed correlated harmonics cancellation (SCHC) technique, our main focus is to utilize deep learning techniques to solve this challenging problem. Here, we propose an end-to-end deep learning model, called DOANet, for SSL. DOANet is based on a one-dimensional dilated convolutional neural network that computes the azimuth and elevation angles of the target sound source from the raw audio signal. The advantage of using DOANet is that it does not require any hand-crafted audio features or ego-noise reduction for DOA estimation. We then evaluate the SSL performance using the proposed and baseline methods and find that the DOANet shows promising results compared to both the angular spectrum methods with and without SCHC. To evaluate the different methods, we also introduce a well-known parameter\u2014area under the curve (AUC) of cumulative histogram plots of angular deviations\u2014as a performance indicator which, to our knowledge, has not been used as a performance indicator for this sort of problem before.<\/jats:p>","DOI":"10.1186\/s13636-020-00184-2","type":"journal-article","created":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T10:02:58Z","timestamp":1604570578000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["DOANet: a deep dilated convolutional neural network approach for search and rescue with drone-embedded sound source localization"],"prefix":"10.1186","volume":"2020","author":[{"given":"Alif Bin Abdul","family":"Qayyum","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. M. Naimul","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrita","family":"Anika","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Farhan","family":"Shadiq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Mushfiqur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Tariqul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheikh Asif","family":"Imran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahruk","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Ariful","family":"Haque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,5]]},"reference":[{"key":"184_CR1","unstructured":"D. Gilman, M. Easton, Unmanned aerial vehicles in humanitarian response. U. N. Off. Coord. Humanitarian Aff.https:\/\/www.unocha.org\/fr\/publication\/policy-briefs-studies\/unmanned-aerial-vehicles-humanitarian-response. Accessed 22 June 2014."},{"key":"184_CR2","unstructured":"G. Sharma, Armed with drones, aid workers seek faster response to earthquakes, floods. Reuters. Accessed 15 May 2016."},{"key":"184_CR3","doi-asserted-by":"publisher","first-page":"4737","DOI":"10.1109\/IROS.2012.6385608","volume-title":"IEEE International Conference on Intelligent Robots and Systems","author":"M. Basiri","year":"2012","unstructured":"M. Basiri, F. Schill, P. U. Lima, D. Floreano, in IEEE International Conference on Intelligent Robots and Systems. Robust acoustic source localization of emergency signals from Micro Air Vehicles (Institute of Electrical and Electronics Engineers (IEEE)Vilamoura, 2012), pp. 4737\u20134742. \nhttps:\/\/doi.org\/10.1109\/IROS.2012.6385608\n\n."},{"key":"184_CR4","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1109\/IROS.2014.6942813","volume-title":"IEEE International Conference on Intelligent Robots and Systems","author":"T. Ohata","year":"2014","unstructured":"T. Ohata, K. Nakamura, T. Mizumoto, T. Taiki, K. Nakadai, in IEEE International Conference on Intelligent Robots and Systems. Improvement in outdoor sound source detection using a quadrotor-embedded microphone array (Institute of Electrical and Electronics Engineers(IEEE)Chicago, Illinois, 2014), pp. 1902\u20131907. \nhttps:\/\/doi.org\/10.1109\/IROS.2014.6942813\n\n."},{"key":"184_CR5","doi-asserted-by":"publisher","unstructured":"K. Hoshiba, K. Washizaki, M. Wakabayashi, T. Ishiki, M. Kumon, Y. Bando, D. Gabriel, K. Nakadai, H. G. Okuno, Design of UAV-embedded microphone array system for sound source localization in outdoor environments. Sensors (Switzerland) (2017). \nhttps:\/\/doi.org\/10.3390\/s17112535\n\n.","DOI":"10.3390\/s17112535"},{"key":"184_CR6","doi-asserted-by":"publisher","first-page":"2511","DOI":"10.1109\/IROS.2018.8594483","volume-title":"IEEE International Conference on Intelligent Robots and Systems","author":"L. Wang","year":"2018","unstructured":"L. Wang, R. Sanchez-Matilla, A. Cavallaro, in IEEE International Conference on Intelligent Robots and Systems. Tracking a moving sound source from a multi-rotor drone (Institute of Electrical and Electronics Engineers (IEEE)Madrid, 2018), pp. 2511\u20132516. \nhttps:\/\/doi.org\/10.1109\/IROS.2018.8594483\n\n."},{"key":"184_CR7","doi-asserted-by":"publisher","first-page":"5735","DOI":"10.1109\/IROS.2018.8593581","volume-title":"IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)","author":"M. Strauss","year":"2018","unstructured":"M. Strauss, P. Mordel, V. Miguet, A. Deleforge, in IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). DREGON: dataset and methods for UAV-embedded sound source localization (IEEEMadrid, Spain, 2018), pp. 5735\u20135742. \nhttps:\/\/doi.org\/10.1109\/IROS.2018.8593581\n\n. \nhttps:\/\/hal.inria.fr\/hal-01854878\n\n."},{"key":"184_CR8","doi-asserted-by":"publisher","first-page":"3943","DOI":"10.1109\/IROS.2013.6696920","volume-title":"IEEE International Conference on Intelligent Robots and Systems","author":"K. Furukawa","year":"2013","unstructured":"K. Furukawa, K. Okutani, K. Nagira, T. Otsuka, K. Itoyama, K. Nakadai, H. G. Okuno, in IEEE International Conference on Intelligent Robots and Systems. Noise correlation matrix estimation for improving sound source localization by multirotor UAV (Institute of Electrical and Electronics Engineers (IEEE)Tokyo, 2013), pp. 3943\u20133948. \nhttps:\/\/doi.org\/10.1109\/IROS.2013.6696920\n\n."},{"key":"184_CR9","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1109\/IROS.2016.7759212","volume-title":"IEEE International Conference on Intelligent Robots and Systems","author":"A. Schmidt","year":"2016","unstructured":"A. Schmidt, A. Deleforge, W. Kellermann, in IEEE International Conference on Intelligent Robots and Systems. Ego-noise reduction using a motor data-guided multichannel dictionary (Institute of Electrical and Electronics Engineers (IEEE)Daejeon, 2016), pp. 1281\u20131286. \nhttps:\/\/doi.org\/10.1109\/IROS.2016.7759212\n\n."},{"issue":"8","key":"184_CR10","doi-asserted-by":"publisher","first-page":"2447","DOI":"10.1109\/jsen.2017.2669262","volume":"17","author":"L. Wang","year":"2017","unstructured":"L. Wang, A. Cavallaro, Microphone-array ego-noise reduction algorithms for auditory micro aerial vehicles. IEEE Sensors J.17(8), 2447\u20132455 (2017). \nhttps:\/\/doi.org\/10.1109\/jsen.2017.2669262\n\n.","journal-title":"IEEE Sensors J."},{"key":"184_CR11","unstructured":"P. Marmaroli, X. Falourd, H. Lissek, in Acoustics 2012. A UAV motor denoising technique to improve localization of surrounding noisy aircrafts: proof of concept for anti-collision systems (Nantes, 2012). \nhttps:\/\/hal.archives-ouvertes.fr\/hal-00811003\n\n."},{"key":"184_CR12","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1109\/ICCE.2016.7430588","volume-title":"2016 IEEE International Conference on Consumer Electronics, ICCE 2016","author":"S. Yoon","year":"2016","unstructured":"S. Yoon, S. Park, S. Yoo, in 2016 IEEE International Conference on Consumer Electronics, ICCE 2016. Two-stage adaptive noise reduction system for broadcasting multicopters (Institute of Electrical and Electronics Engineers (IEEE)Las Vegas, 2016), pp. 219\u2013222. \nhttps:\/\/doi.org\/10.1109\/ICCE.2016.7430588\n\n."},{"key":"184_CR13","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/IROS.2016.7759215","volume-title":"IEEE International Conference on Intelligent Robots and Systems","author":"T. Morito","year":"2016","unstructured":"T. Morito, O. Sugiyama, R. Kojima, K. Nakadai, in IEEE International Conference on Intelligent Robots and Systems. Partially shared deep neural network in sound source separation and identification using a uav-embedded microphone array (Institute of Electrical and Electronics Engineers (IEEE)Daejeon, 2016), pp. 1299\u20131304. \nhttps:\/\/doi.org\/10.1109\/IROS.2016.7759215\n\n."},{"key":"184_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IWAENC.2018.8521324","volume-title":"16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings","author":"B. Yen","year":"2018","unstructured":"B. Yen, Y. Hioka, B. Mace, in 16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings. Improving power spectral density estimation of unmanned aerial vehicle rotor noise by learning from non-acoustic information (Institute of Electrical and Electronics Engineers (IEEE)Tokyo, 2018), pp. 1\u20135. \nhttps:\/\/doi.org\/10.1109\/IWAENC.2018.8521324\n\n."},{"issue":"10","key":"184_CR15","doi-asserted-by":"publisher","first-page":"3418","DOI":"10.3390\/s18103418","volume":"18","author":"J. M. Vera-Diaz","year":"2018","unstructured":"J. M. Vera-Diaz, D. Pizarro, J. Macias-Guarasa, Towards end-to-end acoustic localization using deep learning: from audio signals to source position coordinates. Sensors (Switzerland). 18(10), 3418 (2018). \nhttps:\/\/doi.org\/10.3390\/s18103418\n\n.","journal-title":"Sensors (Switzerland)"},{"issue":"1","key":"184_CR16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.20965\/jrm.2017.p0037","volume":"29","author":"N. Yalta","year":"2017","unstructured":"N. Yalta, K. Nakadai, T. Ogata, Sound source localization using deep learning models. J. Robot. Mechatron.29(1), 37\u201348 (2017). \nhttps:\/\/doi.org\/10.20965\/jrm.2017.p0037\n\n.","journal-title":"J. Robot. Mechatron."},{"key":"184_CR17","unstructured":"F. Yu, V. Koltun, Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2016)."},{"key":"184_CR18","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1109\/jbhi.2018.2818620","volume":"23","author":"M. Anthimopoulos","year":"2019","unstructured":"M. Anthimopoulos, S. Christodoulidis, L. Ebner, T. Geiser, A. Christe, S. Mougiakakou, Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE J. Biomed. Health Inform.23:, 714\u2013722 (2019). \nhttps:\/\/doi.org\/10.1109\/jbhi.2018.2818620\n\n.","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"184_CR19","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/ICASSP.2019.8683802","volume-title":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"S. Hossain","year":"2019","unstructured":"S. Hossain, S. Najeeb, A. Shahriyar, Z. Abdullah, M. Haque, in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). A pipeline for lung tumor detection and segmentation from ct scans using dilated convolutional neural networks (Institute of Electrical and Electronics Engineers (IEEE)Brighton, 2019), pp. 1348\u20131352. \nhttps:\/\/doi.org\/10.1109\/ICASSP.2019.8683802\n\n."},{"issue":"5","key":"184_CR20","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1109\/msp.2019.2924687","volume":"36","author":"A. Deleforge","year":"2019","unstructured":"A. Deleforge, D. Di Carlo, M. Strauss, R. Serizel, L. Marcenaro, Audio-based search and rescue with a drone: highlights from the ieee signal processing cup 2019 student competition [sp competitions]. IEEE Signal Proc. Mag.36(5), 138\u2013144 (2019). \nhttps:\/\/doi.org\/10.1109\/msp.2019.2924687\n\n.","journal-title":"IEEE Signal Proc. Mag."},{"issue":"8","key":"184_CR21","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.1016\/j.sigpro.2011.09.032","volume":"92","author":"C. Blandin","year":"2012","unstructured":"C. Blandin, A. Ozerov, E. Vincent, Multi-source TDOA estimation in reverberant audio using angular spectra and clustering. Sig. Process. 92(8), 1950\u20131960 (2012). \nhttps:\/\/doi.org\/10.1016\/j.sigpro.2011.09.032\n\n.","journal-title":"Sig. Process"},{"issue":"8","key":"184_CR22","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1109\/IWAENC.2018.8521324","volume":"57","author":"J. Capon","year":"1969","unstructured":"J. Capon, High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE. 57(8), 1408\u20131418 (1969). \nhttps:\/\/doi.org\/10.1109\/IWAENC.2018.8521324\n\n.","journal-title":"Proc. IEEE"},{"issue":"4096","key":"184_CR23","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1038\/161686a0","volume":"161","author":"M. S. Bartlett","year":"1948","unstructured":"M. S. Bartlett, Smoothing periodograms from time-series with continuous spectra. Nature. 161(4096), 686\u2013687 (1948).","journal-title":"Nature"},{"key":"184_CR24","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/79.526899","volume":"13","author":"H. Krim","year":"1996","unstructured":"H. Krim, M. Viberg, Two decades of array signal processing research: the parametric approach. IEEE Signal Proc. Mag.13:, 67\u201394 (1996).","journal-title":"IEEE Signal Proc. Mag."},{"key":"184_CR25","unstructured":"Avd Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, K. Kavukcuoglu, Wavenet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)."},{"key":"184_CR26","doi-asserted-by":"publisher","first-page":"5549","DOI":"10.1109\/ICASSP.2018.8461921","volume-title":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"S. Y. Chang","year":"2018","unstructured":"S. Y. Chang, B. Li, G. Simko, T. N. Sainath, A. Tripathi, A. Van Den Oord, O. Vinyals, in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Temporal modeling using dilated convolution and gating for voice-activity-detection (Institute of Electrical and Electronics Engineers (IEEE)Calgary, 2018), pp. 5549\u20135553. \nhttps:\/\/doi.org\/10.1109\/ICASSP.2018.8461921\n\n."},{"key":"184_CR27","unstructured":"S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)."},{"key":"184_CR28","unstructured":"S. Santurkar, D. Tsipras, A. Ilyas, A. Madry, in Advances in Neural Information Processing Systems, ed. by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett. How does batch normalization help optimization? (Curran Associates, Inc., 2018), pp. 2483\u20132493."},{"key":"184_CR29","unstructured":"IEEE Signal Processing Cup 2019. \nhttp:\/\/dregon.inria.fr\/datasets\/signal-processing-cup-2019\n\n. Accessed 22 Oct 2020."},{"key":"184_CR30","unstructured":"J. S. Garofolo, Timit acoustic phonetic continuous speech corpus. Web Download. Linguist. Data Consortium, 1993 (1993)."},{"key":"184_CR31","doi-asserted-by":"publisher","unstructured":"R. Scheibler, E. Bezzam, I. Dokmanic, Pyroomacoustics: a python package for audio room simulation and array processing algorithms (Institute of Electrical and Electronics Engineers (IEEE), Calgary, 2018). \nhttps:\/\/doi.org\/10.1109\/icassp.2018.8461310\n\n.","DOI":"10.1109\/icassp.2018.8461310"},{"issue":"1","key":"184_CR32","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/TAP.1982.1142739","volume":"30","author":"L. Griffiths","year":"1982","unstructured":"L. Griffiths, C. Jim, An alternative approach to linearly constrained adaptive beamforming. IEEE Trans. Antennas Propag.30(1), 27\u201334 (1982). \nhttps:\/\/doi.org\/10.1109\/TAP.1982.1142739\n\n.","journal-title":"IEEE Trans. Antennas Propag."},{"key":"184_CR33","volume-title":"Deep Learning with Python","author":"F. Chollet","year":"2018","unstructured":"F. Chollet, Deep Learning with Python, 1st (Manning Publications Co., New York, 2018)."},{"key":"184_CR34","unstructured":"M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al., Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)."},{"key":"184_CR35","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1145\/1553374.1553380","volume-title":"Proceedings of the 26th Annual International Conference on Machine Learning ICML \u201909","author":"Y. Bengio","year":"2009","unstructured":"Y. Bengio, J. Louradour, R. Collobert, J. Weston, in Proceedings of the 26th Annual International Conference on Machine Learning ICML \u201909. Curriculum learning (Association for Computing MachineryNew York, NY, USA, 2009), pp. 41\u201348. \nhttps:\/\/doi.org\/10.1145\/1553374.1553380\n\n. https:\/\/doi.org\/10.1145\/1553374.1553380."},{"key":"184_CR36","unstructured":"D. P. Kingma, J. Ba, Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2017)."}],"container-title":["EURASIP Journal on Audio, Speech, and Music Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13636-020-00184-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13636-020-00184-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13636-020-00184-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T10:22:06Z","timestamp":1604571726000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13636-020-00184-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,5]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["184"],"URL":"https:\/\/doi.org\/10.1186\/s13636-020-00184-2","relation":{},"ISSN":["1687-4722"],"issn-type":[{"value":"1687-4722","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,5]]},"assertion":[{"value":"4 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16"}}