{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:50:55Z","timestamp":1777639855413,"version":"3.51.4"},"reference-count":12,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T00:00:00Z","timestamp":1534464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1736215, 61672302"],"award-info":[{"award-number":["U1736215, 61672302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Natural Science Foundation","award":["LZ15F020002, LY17F020010"],"award-info":[{"award-number":["LZ15F020002, LY17F020010"]}]},{"DOI":"10.13039\/100007834","name":"Ningbo Natural Science Foundation","doi-asserted-by":"publisher","award":["2017A610123"],"award-info":[{"award-number":["2017A610123"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningbo University Fund","award":["XKXL1509, XKXL1503"],"award-info":[{"award-number":["XKXL1509, XKXL1503"]}]},{"name":"Mobile Network Application Technology Key Laboratory of Zhejiang Province","award":["F2018001"],"award-info":[{"award-number":["F2018001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the widespread availability of cell-phone recording devices, source cell-phone identification has become a hot topic in multimedia forensics. At present, the research on the source cell-phone identification in clean conditions has achieved good results, but that in noisy environments is not ideal. This paper proposes a novel source cell-phone identification system suitable for both clean and noisy environments using spectral distribution features of constant Q transform (CQT) domain and multi-scene training method. Based on the analysis, it is found that the identification difficulty lies in different models of cell-phones of the same brand, and their tiny differences are mainly in the middle and low frequency bands. Therefore, this paper extracts spectral distribution features from the CQT domain, which has a higher frequency resolution in the mid-low frequency. To evaluate the effectiveness of the proposed feature, four classification techniques of Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN) and Recurrent Neuron Network-Long Short-Term Memory Neural Network (RNN-BLSTM) are used to identify the source recording device. Experimental results show that the features proposed in this paper have superior performance. Compared with Mel frequency cepstral coefficient (MFCC) and linear frequency cepstral coefficient (LFCC), it enhances the accuracy of cell-phones within the same brand, whether the speech to be tested comprises clean speech files or noisy speech files. In addition, the CNN classification effect is outstanding. In terms of models, the model is established by the multi-scene training method, which improves the distinguishing ability of the model in the noisy environment than single-scenario training method. The average accuracy rate in CNN for clean speech files on the CKC speech database (CKC-SD) and TIMIT Recaptured Database (TIMIT-RD) databases increased from 95.47% and 97.89% to 97.08% and 99.29%, respectively. For noisy speech files with seen noisy types and unseen noisy types, the performance was greatly improved, and most of the recognition rates exceeded 90%. Therefore, the source identification system in this paper is robust to noise.<\/jats:p>","DOI":"10.3390\/info9080205","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T10:54:25Z","timestamp":1534503265000},"page":"205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Source Cell-Phone Identification in the Presence of Additive Noise from CQT Domain"],"prefix":"10.3390","volume":"9","author":[{"given":"Tianyun","family":"Qin","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering of Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rangding","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering of Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diqun","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering of Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering of Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/TIFS.2011.2178403","article-title":"Recognition of Brand and Models of Cell-Phones from Recorded Speech Signals","volume":"7","author":"Hanilci","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hanil\u00e7i, C., and Ertas, F. (2013, January 17\u201319). Optimizing Acoustic Features for Source Cell-Phone Recognition Using Speech Signals. Proceedings of the First ACM Workshop on Information Hiding and Multimedia Security, Montpellier, France.","DOI":"10.1145\/2482513.2482520"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kotropoulos, C., and Samaras, S. (2014, January 20\u201323). Mobile Phone Identification Using Recorded Speech Signals. Proceedings of the 19th International Conference on Digital Signal Processing, Hong Kong, China.","DOI":"10.1109\/ICDSP.2014.6900732"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.dsp.2014.08.008","article-title":"Source Cell-Phone Recognition from Recorded Speech Using Non-speech Segments","volume":"35","author":"Kinnunen","year":"2014","journal-title":"Digital Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zou, L., Yang, J., and Huang, T. (2014, January 9\u201313). Automatic cell phone recognition from speech recordings. Proceedings of the 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Xi\u2019an, China.","DOI":"10.1109\/ChinaSIP.2014.6889318"},{"key":"ref_6","first-page":"191","article-title":"A Recording Device Identification Algorithm Based on Improved PNCC Feature and Two-Step Discriminative Training","volume":"42","author":"He","year":"2014","journal-title":"Electron. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kotropoulos, C. (2013, January 4\u20135). Telephone Handset Identification Using Sparse Representations of Spectral Feature Sketches. Proceedings of the 2013 International Workshop on Biometrics and Forensics (IWBF), Lisbon, Portugal.","DOI":"10.1109\/IWBF.2013.6547326"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jin, C., Wang, R., Yan, D., Tao, B., Chen, Y., and Pei, A. (2016, January 17\u201319). Source Cell-Phone Identification Using Spectral Features of Device Self-noise. Proceedings of the 15th International Workshop on Digital Watermarking (IWDW), Beijing, China.","DOI":"10.1007\/978-3-319-53465-7_3"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qi, S., Huang, Z., Li, Y., and Shi, S. (2016, January 13\u201315). Audio Recording Device Identification Based on Deep Learning. Proceedings of the 2016 IEEE International Conference on Signal and Image Processing (ICSIP), Beijing, China.","DOI":"10.1109\/SIPROCESS.2016.7888298"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1109\/TIFS.2018.2812185","article-title":"Band Energy Difference for Source Attribution in Audio Forensics","volume":"13","author":"Luo","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_11","unstructured":"Jin, C. (2018, August 17). Research on Passive Forensics for Digital Audio. 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NIST Speech Disc 1-1.1.","DOI":"10.6028\/NIST.IR.4930"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/8\/205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:19:21Z","timestamp":1760195961000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/8\/205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,17]]},"references-count":12,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["info9080205"],"URL":"https:\/\/doi.org\/10.3390\/info9080205","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,17]]}}}