{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T19:30:22Z","timestamp":1790969422391,"version":"4.1.0"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T00:00:00Z","timestamp":1577404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2014one meter, two meters, and three meters\u2014irrespective 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20010172","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T11:42:47Z","timestamp":1577446967000},"page":"172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach"],"prefix":"10.3390","volume":"20","author":[{"given":"Mariam","family":"Yiwere","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Hanbat National University, Daejeon 34158, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Joo","family":"Rhee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Hanbat National University, Daejeon 34158, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,27]]},"reference":[{"key":"ref_1","unstructured":"Murray, C.J., Erwin, H., and Wermter, S. (2004, January 20). Robotic Sound-Source Localization and Tracking Using Interaural Time Difference and Cross-Correlation. Proceedings of the AI Workshop on NeuroBotics, Ulm, Germany."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, L., and Cavallaro, A. (2017, January 5\u20139). Time-Frequency Processing for Sound Source Localization from a Micro Aerial Vehicle. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952205"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chakrabarty, S., and Habets, E.A.P. (2017, January 15\u201318). Broadband DOA Estimation using Convolutional Neural Networks Trained with Noise Signals. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA.","DOI":"10.1109\/WASPAA.2017.8170010"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ferguson, E.L., Williams, S.B., and Jin, C.T. (2018, January 15\u201320). Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462024"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chakrabarty, S., and Habets, E.A.P. (2017, January 4\u20139). Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.","DOI":"10.1109\/WASPAA.2017.8170010"},{"key":"ref_6","unstructured":"Roden, R., Moritz, N., Gerlach, S., Weinzierl, S., and Goetze, S. (2015, January 16\u201319). On Sound Source Localization of Speech Signals using Deep Neural Networks. Proceedings of the 41st Deutsche Jahrestagung fur Akustik Conference (DAGA), Nuremberg, Germany."},{"key":"ref_7","unstructured":"Lee, S., Park, Y., and Park, Y. (2015). Three-dimensional Sound Source Localization Using Inter-Channel Time Difference Trajectory. Int. J. Adv. Robot. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rodemann, T., Ince, G., Joublin, F., and Goerick, C. (2008, January 22\u201326). Using Binaural and Spectral Cues for Azimuth and Elevation Localization. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650667"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Perotin, L., Serizel, R., Vincent, E., and Guerin, A. (2018, January 17\u201320). CRNN-Based Joint Azimuth and Elevation Localization with the Ambisonics Intensity Vector. Proceedings of the 16th International Workshop on Acoustic Signal Enhancement (IWAENC), Tokyo, Japan.","DOI":"10.1109\/IWAENC.2018.8521403"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9922","DOI":"10.3390\/s140609922","article-title":"\u201cSmartMonitor\u201d\u2014An Intelligent Security System for the Protection of Individuals and Small Property with the Possibility of Home Automation","volume":"14","author":"Frejlichowski","year":"2014","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Djahel, S., Smith, N., Wang, S., and Murphy, J. (2015, January 25\u201328). Reducing emergency services response time in smart cities: An advanced adaptive and fuzzy approach. Proceedings of the IEEE First International Smart Cities Conference, Guadalajara, Mexico.","DOI":"10.1109\/ISC2.2015.7366151"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/3081048","article-title":"On Indexicality, Direction of Arrival of Sound Sources and Human\u2013Robot Interaction","volume":"2016","author":"Meza","year":"2016","journal-title":"J. Robot."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Do, H.M., Sheng, W., and Liu, M. (2016). Human-assisted sound event recognition for home service robots. Robot. Biomim., 3.","DOI":"10.1186\/s40638-016-0042-2"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, T., Mustiere, F., and Micheyl, C. (2016, January 16\u201320). Intelligent Hearing Aids: The Next Revolution. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology (EMBC), Orland, FL, USA.","DOI":"10.1109\/EMBC.2016.7590643"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/TSMCC.2010.2051026","article-title":"A reciprocal and Extensible architecture for multiple-target tracking in a Smart Home","volume":"41","author":"Lu","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.csl.2015.03.003","article-title":"A Survey on Sound Source Localization in Robotics: From Binaural to Array Processing Methods","volume":"34","author":"Sylvain","year":"2015","journal-title":"Comput. Speech Lang."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Samarasinghe, P.N., Abhayapala, T.D., Polettfi, M.A., and Betlehem, T. (2014, January 21\u201323). On Room Impulse Response between Arbitrary Points: An Efficient Parameterization. Proceedings of the 6th International Symposium on Communication, Control and Signal Processing (ISCCSP), Athens, Greece.","DOI":"10.1109\/ISCCSP.2014.6877838"},{"key":"ref_18","unstructured":"Bronkhorst, A.W. (2002, January 16\u201320). Modeling Auditory Distance Perception in Rooms. Proceedings of the AAE Forum Acusticum, Sevilla, Spain."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/TASLP.2016.2601222","article-title":"Direct-to-Reverberant Energy Ratio Estimation using a First-Order Microphone","volume":"25","author":"Chen","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TASL.2010.2050687","article-title":"Binaural Estimation of Sound Source Distance via the Direct-to-Reverberant Energy Ratio for Static and Moving Sources","volume":"18","author":"Lu","year":"2010","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rodemann, T. (2010, January 18\u201322). A Study on Distance Estimation in Binaural Sound Localization. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651455"},{"key":"ref_22","unstructured":"Honda, S., Shinohara, T., Uebo, T., and Nakasako, N. (2016, January 10\u201314). Estimating the Distance to a Sound Source using Single-Channel Cross-Spectral Method between Observed and Pseudo-Observed Waves based on Phase Interference. Proceedings of the 23rd International Congress on Sound & Vibration, Athens, Greece."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TASL.2009.2022001","article-title":"Binaural Sound Source Distance Learning in Rooms","volume":"17","author":"Vesa","year":"2009","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1109\/TASL.2013.2260155","article-title":"Sound Source Distance Estimation in Rooms based on Statistical Properties of Binaural Signals","volume":"21","author":"Georganti","year":"2013","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1109\/TASL.2011.2104953","article-title":"Speaker Distance Detection using a Single Microphone","volume":"19","author":"Georganti","year":"2011","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1121\/1.5000165","article-title":"Source localization in an ocean waveguide using supervised machine learning","volume":"142","author":"Niu","year":"2017","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Brendel, A., and Kellermann, W. (2018, January 15\u201320). Learning\u2013based acoustic source \u2013microphone distance estimation using the coherent-to-diffuse power ratio. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462474"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, Z., Xu, J., Gong, Z., Wang, H., and Yan, Y. (2019). Multiple source localization in a shallow water waveguide exploiting subarray beamforming and deep neural networks. Sensors, 19.","DOI":"10.3390\/s19214768"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1121\/1.5116016","article-title":"Deep-learning source localization using multi-frequency magnitude-only data","volume":"146","author":"Niu","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_30","first-page":"12384","article-title":"Distance Estimation and Localization of Sound Source in Reverberant Conditions using Deep Neural Networks","volume":"12","author":"Yiwere","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., and Liu, T. (2015, January 17\u201321). Document Modeling with Gated Recurrent Neural Network for Sentiment Classification. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1167"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zuo, Z., Shuai, B., Wang, G., Liu, X., Wang, X., Wang, B., and Chen, Y. (2015, January 7\u201312). Convolutional Recurrent Neural Networks: Learning Spatial Dependencies for Image Representation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301268"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1121\/1.1915893","article-title":"A scale for the measurement of the psychological magnitude pitch","volume":"8","author":"Stevens","year":"1937","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Harma, A. (2007, January 27\u201331). Ambient telephony: Scenarios and research challenges. Proceedings of the 8th Annual Conference of the International Speech Communication Association (INTERSPEECH), Antwerp, Belgium.","DOI":"10.21437\/Interspeech.2007-250"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TASLP.2015.2389618","article-title":"Robust Sound Event Classification Using Deep Neural Networks","volume":"23","author":"McLoughlin","year":"2015","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_36","unstructured":"Lim, H., Park, J., Lee, K., and Han, Y. (2017, January 16\u201317). Rare Sound Event Detection using 1D Convolutional Recurrent Neural Networks. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop, Munich, Germany."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201331). Speech Recognition with Deep Recurrent Neural Networks. Proceedings of the IEEE International Conference on Acoustics Speech, and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, W., Motlicek, P., and Odobez, J. (2018, January 21\u201326). Deep Neural Networks for Multiple Speaker Detection and Localization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8461267"},{"key":"ref_39","unstructured":"White, L.S., and King, S. (2019, June 11). The EUSTACE Speech Corpus. Available online: http:\/\/www.cstr.ed.ac.uk\/projects\/eustace."},{"key":"ref_40","unstructured":"Bencina, R., and Burk, P. (2001, January 17\u201322). PortAudio\u2014An Open Source Cross Platform Audio API. Proceedings of the International Computer Music Conference (ICMC), Havana, Cuba."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pandeya, Y.R., Kim, D., and Lee, J. (2018). Domestic Cat Sound Classification using Learned Features from Deep Neural Nets. Appl. Sci., 8.","DOI":"10.3390\/app8101949"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"McFee, B., Raffel, C., Liang, D., Ellis, D.P.W., McVicar, M., Battenberg, E., and Nieto, O. (2015, January 6\u201312). Librosa: Audio and Music Signal Analysis in Python. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"ref_43","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20136). ImageNet Classification with Deep convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Parascandolo, G., Huttunen, H., and Virtanen, T. (2016, January 20\u201325). Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings. Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472917"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Miao, Y., Gowayyed, M., and Metze, F. (2015, January 13\u201317). EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-Based Decoding. Proceedings of the 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Scottsdale, AZ, USA.","DOI":"10.1109\/ASRU.2015.7404790"},{"key":"ref_46","unstructured":"Park, J., Boo, Y., Choi, I., Shin, S., and Sung, W. (2018, January 3\u20138). Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices. Proceedings of the 32nd Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_47","unstructured":"Graves, A., and Schmidhuber, J. (2008, January 8\u201311). Offline Handwritting Recognition with Multidimensional Recurrent Neural Networks. Proceedings of the 22nd Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Nelson, D.M.Q., Pereira, A.C.M., and de Oliveira, R.A. (2017, January 14\u201319). Stock Market\u2019s Price Movement Prediction with LSTM Neural Networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/JSTSP.2018.2885636","article-title":"Sound Event Localization and Detection of Overlapping Sources using Convolutional Recurrent Neural Networks","volume":"13","author":"Adavanne","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B.V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representation using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_51","unstructured":"Chollet, F. (2018, May 23). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_52","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-scale machine learning on heterogeneous systems. arXiv."},{"key":"ref_53","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_54","unstructured":"Kohavi, R. (1995, January 20\u201325). A study of cross-validation and bootstrap for accuracy estimation. Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Jeub, M., Schafer, M., and Vary, P. (2009, January 5\u20137). A binaural room impulse response database for the evaluation of dereverberation algorithms. Proceedings of the 16th International Conference on Digital Signal Processing, Santorini-Hellas, Greece.","DOI":"10.1109\/ICDSP.2009.5201259"},{"key":"ref_56","unstructured":"Kabal, P. (2018). TSP Speech Database, McGill University. [2nd ed.]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:46:11Z","timestamp":1760190371000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,27]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010172"],"URL":"https:\/\/doi.org\/10.3390\/s20010172","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,27]]}}}