International Journal of Speech Technology

, Volume 21, Issue 4, pp 761–771 | Cite as

Onset detection for tar solo

  • Behraz Farrokhi
  • Ehsanollah Kabir
  • Hedieh SajediEmail author


This paper develops a new method of onset detection for the Tar, a traditional Iranian musical instrument. The proposed method is based on both types of pitch and energy features. Therefore, it can be utilized to detect either soft or hard onsets. Through this combination, we obtained a more precise separation between two adjacent notes. This ability is especially useful to detect the reaz, repeatedly played notes with the same frequency and short durations. For the evaluation of the method, a data set with predetermined onsets was produced and the results were compared with an energy-based method explained in terms of F-measure.


Onset detection Iranian music Tar Hard onset Soft onset 


  1. Argenti, F., Nesi, P., & Pantaleo, G. (2011). Automatic transcription of polyphonic music based on the constant-Q bispectral analysis. IEEE Trans. Audio, Speech Language Process, 19(6), 1610–1630.CrossRefGoogle Scholar
  2. Bello, J. P., Daudet, L., andC., S. A., Duxbury, M., Davies, & Sandler, M. (2005). A tutorial on onset detection in musical signals. IEEE Transactions on Speech and Audio Processing, 13(5), 1035–1047.CrossRefGoogle Scholar
  3. Bello, J. P., Duxbury, C., Davies, M., & Sandler, M. B. (2004). On the use of phase and energy for musical onset detection in the complex domain. IEEE Signal Processing Letters, 11(6), 553–556.CrossRefGoogle Scholar
  4. Bello, J. P., & Sandler, M. (2003). Phase-based note onset detection for music signals. In IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE Cat. No. 03TH8684), pp. 441–444.Google Scholar
  5. Benetos, E., Dixon, S., Giannoulis, D., Kirchhoff, H., & Klapuri, A. (2013). Automatic music transcription: Challenges and future directions. Journal of Intelligent Information Systems, 41(3), 407–434.CrossRefGoogle Scholar
  6. Benetos, E., & Stylianou, Y. (2010). Auditory spectrum-based pitched instrument onset detection. IEEE Transactions on Audio, Speech, and Language Processing, 18(8), 1968–1977.CrossRefGoogle Scholar
  7. Bhalke, D. G., Rama Rao, C. B., & Borman, D. S. (2016). Automatic musical instrument classification using fractional fourier transform based- MFCC features and counter propagation neural network. Journal of Intelligent Information, 46(16), 445–446.Google Scholar
  8. Böck, S., Arzt, A., Krebs, F., & Schedl, M., Online realtime onset detection with recurrent neural networks, In Proceedings of the 15th International Conference on Digital Audio Effects, pp. 15–18, 2012.Google Scholar
  9. Bock, S., & Schedl, M. (2012). Polyphonic piano note transcription with recurrent neural networks, In ICASSP, IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 121–124.Google Scholar
  10. Bock, S., & Widmer, G. (2013). Maximum filter vibrato suppression for onset detection, In Proceedings of the 16th International Conference on Digital Audio Effects, pp. 1–7.Google Scholar
  11. Bouguelia, M. R., Nowaczyk, S., Santosh, K. C., & Verikas, A. (2017). Agreeing to disagree: Active learning with noisy labels without crowdsourcing. International Journal of Machine Learning and Cybernetics, 9(8), 1307–1319.CrossRefGoogle Scholar
  12. Brown, J. C. (1992). Musical fundamental frequency tracking using a pattern recognition method. Journal of the Acoustical Society of America, 92(3), 1394–1402CrossRefGoogle Scholar
  13. Brown, J. C., & Puckette, M. S. (1992). An efficient algorithm for the calculation of a constant Q transform. Journal of the Acoustical Society of America, 92(5), 2698–2701.CrossRefGoogle Scholar
  14. Collins, N. (2005). Using a pitch detector for onset detection. In International Symposium on Music Information Retrieval, pp. 100–106.Google Scholar
  15. Collins, N. (2005). A comparison of sound onset detection algorithms with emphasis on psychoacoustically motivated detection functions. Audio Engineering Society Convention 118, 1, 34–45.Google Scholar
  16. Degara, N., Davies, M. E. P., Pena, A., & Plumbley, M. D. (2011). Onset event decoding exploiting the rhythmic structure of polyphonic music. IEEE Journal of Selected Topics in Signal Processing, 5(6), 1228–1239.CrossRefGoogle Scholar
  17. Dixon, S. (2006). Onset detection revisited. In Proceedings of the 9th International Conference on Digital Audio Effects, pp. 1–6.Google Scholar
  18. Duxbury, C., Sandler, M., & Davies, M. (2002). A hybrid approach to musical note onset detection. In 5th International Conference on Digital Audio Effects (DAFx-02), Hamburg, Germany, pp. 33–38.Google Scholar
  19. Gainza, M., & Coyle, E. (2011). Tempo detection using a hybrid multiband approach. IEEE Transactions on Audio, Speech, and Language Processing, 19(1), 57–68.CrossRefGoogle Scholar
  20. Heo, H., Sung, D., Lee, K. (2013). Note onset detection based on harmonic cepstrum regularity. In IEEE International Conference on Multimedia and Expo (ICME), San Jose, CA, USA, pp. 1–6,.Google Scholar
  21. Heydarisan, P. (2016). Automatic recognition of Persian musical models in audio musical signals. Doctoral thesis, London Metropolitan University.Google Scholar
  22. Klapuri, A. (1999). Sound onset detection by applying psychoacoustic knowledge. In IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASS P99 (Cat. No.99CH36258), Vol. 6, pp. 3089–3092.Google Scholar
  23. Liang, C., Su, L., & Yang, Y. (2015). Musical onset detection using constrained linear reconstruction. IEEE Signal Processing Letters, 22(11), 2142–2146.CrossRefGoogle Scholar
  24. Marchi, E., Ferroni, G., Eyben, F., & Squartini, S. (2014). Audio onset detection: A wavelet packet based approach with recurrent neural networks. In International Joint Conference on Neural Networks (IJCNN), Beijing, China.Google Scholar
  25. Masri, P. (1996). Computer modelling of sound for transformation and synthesis of musical signals. (Doctoral dissertation, University of Bristol).Google Scholar
  26. Oliveira, J. L., Davies, M. E. P., Gouyon, F., & Reis, L. P. (2012). Beat tracking for multiple applications: A multi-agent system architecture with state recovery. IEEE Transactions on Audio, Speech, Language Processing, 20(10), 2696–2706.CrossRefGoogle Scholar
  27. Percival, G., & Tzanetakis, G. (2014). Streamlined tempo estimation based on autocorrelation and cross-correlation with pulses. IEEE/ACM Transactions on Speech and Language Processing, 22(12), 1765–1776.CrossRefGoogle Scholar
  28. Reis, G., Fernandéz, F., De Vega, & Ferreira, A. (2012). Automatic transcription of polyphonic piano music using genetic algorithms, adaptive spectral envelope modeling, and dynamic noise level estimation. IEEE Transactions on Speech and Language Processing, 20(8), 2313–2328.CrossRefGoogle Scholar
  29. Robinson, D. W., & Dadson, R. S. (1956). A re-determination of the equal-loudness relations for pure tones. British Journal of Applied Physics, 7(5), 166–181.CrossRefGoogle Scholar
  30. Santosh, K., Hangarge, M., Bevilacqua, V., & Negi, A. (2017). A Fast k-Nearest Neighbor Classifier Using Unsupervised Clustering. In: Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016, Communications in Computer and Information Science, Vol. 709. Singapore: Springer.Google Scholar
  31. Schloss, A. W. (1985). On the Automatic Transcription of Percussive Music - From Acoustic Signal to High-Level Analysis. Doctoral thesis. Stanford University.Google Scholar
  32. Sephus, N. H., Lanterman, A. D., & Anderson, D. V. (2014). Modulation spectral features: In pursuit of invariant representations of music with application to unsupervised source identification. Journal of New Music Research, 44(1), 58–70.CrossRefGoogle Scholar
  33. Sigtia, S., Benetos, E., & Dixon, S. (2016). An end-to-end neural network for polyphonic piano music transcription. IEEE/ACM Transactions on Audio, Speech and Language Processing, 24(5), 927–939.CrossRefGoogle Scholar
  34. Stasiak, B., Mońko, J., & Niewiadomski, A. (2016). Note onset detection in musical signals via neural-network-based multi-ODF fusion. International Journal of Applied Mathematics and Computer Science, 26(1), 203–213.MathSciNetCrossRefGoogle Scholar
  35. Stylianou, Y., & Gedik, A. C. (2010). Three dimensions of pitched instrument onset detection. IEEE Transactions on Audio, Speech and Language Processing, 18(6), 1517–1527.CrossRefGoogle Scholar
  36. Thoshkahna, B., & Ramakrishnan, K. R. (2008). A psychoacoustics based sound onset detection algorithm for polyphonic audio music and audio. In 9th International Conference on Signal Processing (ICSP), pp. 1424–1427.Google Scholar
  37. Tian, M., Black, D. A. A., & Sandler, M. (2014). Design and evaluation of onset detectors using different fusion policies. In 15th International Society for Music Information Retrieval Conference (ISMIR 2014) Design, Ismir, pp. 631–636.Google Scholar
  38. Todisco, M., Delgado, H., & Evans, N. (2016). A new feature for automatic speaker verification anti-spoofing: constant Q cepstral coefficients. In Speaker Odyssey Workshop, Bilbao, Spain.Google Scholar
  39. Todisco, M., Delgado, H., & Evans, N. (2017). Constan Q cepstral coefficients: A spoofing countermeasure for automatic speacker verification. Computer Speech & Language, 45, 516–535.CrossRefGoogle Scholar
  40. Zhou, R., Mattavelli, M., & Zoia, G. (2008). Music onset detection based on resonator time frequency image. IEEE Transactions on Audio, Speech and Language Processing, 16(8), 1685–1695.CrossRefGoogle Scholar
  41. Zhou, R., & Reiss, J. D. (2007). Music onset detection combining energy-based and pitch-based approaches. In Proceedings MIREX Audio Onset Detection Contest.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Behraz Farrokhi
    • 1
  • Ehsanollah Kabir
    • 2
  • Hedieh Sajedi
    • 3
    Email author
  1. 1.Electrical Engineering DepartmentIran University of Science and TechnologyTehranIran
  2. 2.Electrical Engineering DepartmentTarbiat Modares UniversityTehranIran
  3. 3.School of Mathematics, Statistics and Computer Science, College of ScienceUniversity of TehranTehranIran

Personalised recommendations