نوا: دادگان موسیقی سنتی ایرانی برای تشخیص دستگاه و سازهای اصیل ایرانی

[1] Y. V. Murthy, and S.G. Koolagudi, “Content-based music information retrieval (cb-mir) and its applications toward the music industry: A review,” ACM Computing Surveys, vol. 51, no. 3, 2018.

[2] A. Bonjyotsna and M. Bhuyan, “Signal processing for segmentation of vocal and non-vocal regions in songs: A review,” International Conference on Signal Processing Image Processing and Pattern Recognition, Los Alamitos, CA, pp. 87–91, 2013.

[3] T. Zhang, “Automatic singer identification,” International Conference on Multimedia and Expo., vol. 1, pp. 1-33, 2003.

[4] N. Scaringella, G. Zoia, and D. Mlynek, “Automatic genre classification of music content: A survey,” IEEE Signal Process. Mag. vol. 23, no. 2, pp. 133–141, 2006.

[5] A. Kotsifakos, P. Papapetrou, J. Hollmen, D. Gunopulos, and V. Athitsos,  “A survey of query-by-humming similarity methods, ” In Proceedings of the 5th International Conference on Pervasive Technologies Related to Assistive Environments. ACM, New York, NY, 5. 2012

[6] B. Y. Chua,  Automatic Extraction of Perceptual Features and Categorization of Music Emotional Expressionsfrom Polyphonic Music Audio Signals, Ph.D. Thesis, Monash University, 2008.

[7] A. Eronen, Automatic Musical Instrument Recognition, Master’s Thesis, Department of Information Technology, Tampere University of Technology, 2001

[8] Z. Fu, G. Lu, K. M. Ting, and D. Zhang, ,  “A survey of audio-based music classification and annotation,” IEEE Trans. Multimed. Vol. 13, no. 2, pp. 303–319, 2011

[9] L. Miller, Music and Song in Persia (RLE Iran B): The Art of Avaz. Routledge, 2012.

[10] سار محمودان، ایوب بنوشی،  «دسته‌بندی خودکار گام ماهور موسیقی ایرانی توسط یک شبکه عصبی مصنوعی»،  دومین کنفرانس بین المللی آکوستیک و ارتعاشات، دانشگاه صنعتی شریف، دیماه 1391

[11] صابر عبداله زادگان، شهرام جعفری، مرتضی دیرند، «تشخیص خودکار دستگاه و گام موسیقی سنتی ایرانی مبتنی بر تکنوازی سازهای تار و سنتور به وسیله استخراج نت هوشمند»، بیستمین کنفرانس ملی سالانه انجمن کامپیوتر ایران، دانشگاه فردوسی مشهد، اسفند 1393

[12] M. A. Layegh, S. Haghipour, & Y. N. Sarem, “Classification of the Radif of Mirza Abdollah a canonic repertoire of Persian music using SVM method,” Gazi University Journal of Science Part A: Engineering and Innovation, vol. 1, no. 4, pp. 57-66, 2013.

[13] S. R. Azar, A. Ahmadi, S. Malekzadeh, and M. Samami, “Instrument-Independent Dastgah Recognition of Iranian Classical Music Using AzarNet,” arXiv preprint arXiv:1812.07017, 2018.

[14] S. Abdoli, “Iranian Traditional Music Dastgah Classification,” In ISMIR, pp. 275-280, 2011.

[15] B. Beigzadeh, M. Belali Koochesfahani, “Classification of Iranian traditional musical modes (DASTGÄH) with artificial neural network,” Journal of Theoretical and Applied Vibration and Acoustics, vol. 2, no. 2, pp.107-118, 2016.

[16] H. Hajimolahoseini, R. Amirfattahi, and M. Zekri, “Real-time classification of Persian musical Dastgahs using artificial neural network,” In The 16th CSI International Symposium on Artificial Intelligence and Signal Processing, pp. 157-160,  2012.

[17] H. Farhat, The dastgah concept in Persian music, Cambridge University Press, 2004.

[18] B. Logan, “Mel Frequency Cepstral Coefficients for Music Modeling,” In ISMIR, vol. 270,  pp. 1-11, 2000

[19] N. Dehak,  P. Kenny, R. Dehak, and et al. “Front-end factor analysis for speaker verification,” IEEE Trans. Audio Speech Lang. Process., vol. 19, no. 4, pp. 788–798, 2011.

[20] P. Kenny, G. Boulianne, P. Ouellet, and et al. “Joint factor analysis versus eigenchannels in speaker recognition,” IEEE Trans. Audio Speech Lang. Process., vol 15, no. 4, pp. 1435–1447, 2007.

[21] N. Dehak, P. A. Torres-Carrasquillo, D. A. Reynolds, and et al. “Language recognition via i-vectors and dimensionality reduction,” InterSpeech, pp. 857–860, 2011.

[22] M. H. Bahari, R. Saeidi, D. Van Leeuwen, and et al. “Accent recognition using i-vector, Gaussian mean supervector and Gaussian posterior probability supervector for spontaneous telephone speech,” IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), pp. 7344–7348, 2013.

[23] R. Xia, Y. Liu, “Using i-vector space model for emotion recognition,” InterSpeech, 2012.

[24] H. Eghbal-zadeh, B. Lehner, M. Dorfer, and et al. “CP-JKU submissions for DCASE-2016: a hybrid approach using binaural i-vectors and deep convolutional neural networks,” 2016.

[25] X. Wei, and L. Wenju, “Multilingual I-Vector based Statistical Modeling for Music Genre Classification,” InterSpeech, 2017.

[26] H. Zeinali, B. BabaAli, and H. Hadian,“Online signature verification using i-vector representation. IET Biometrics,” vol. 7, no. 5, pp. 405-414, 2017.