Deteksi Onset Gamelan Bebasis DWPT dan BLSTM

https://doi.org/10.22146/ijeis.79534

Hisyam Mustofa(1*), Agfianto Eko Putra(2)

(1) Magister Ilmu Komputer, UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Gamelan consists of various kinds of instruments that have different characteristics. Each has characteristics in terms of the basic frequency, amplitude, signal envelope, and different ways of playing it, resulting in differences in the sustain power of the signal. These characteristics cause the problem of vanishing gradient in the Elman Network model which was used in previous studies in studying the onset detection in the Saron instrument signal which has an average interval of more than 0.6 seconds. This study uses BLSTM (Bidirectional Long Short Term Memory) as a model for training and Wavelet Packet Transformation to design a psychoacoustic critical bandwidth as a model for feature extraction. For the peak picking method, this study uses a fixed threshold method with a value of 0.25. The use of the BLSTM model supported by the Wavelet Packet Transform is expected to overcome the vanishing gradient that exists in a simple RNN architecture. The model was tested based on 3 evaluation parameters, namely precision, recall and F-Measure. Based on the test scenario carried out, the model can overcome the vanishing gradient problem on the Saron instrument which has an average interval between onset of 600 ms. Out of a total of 428 onsets on the Saron instrument, the model successfully detected 426 correctly, with 4 incorrectly detected onsets and 2 undetected onsets. A thorough evaluation for each of the precision, recall, and F1-Measure algorithms obtained 0.975, 0.945 and 0.960.


Keywords


Onset Detaction; Wavelet Packet Transformation; Bidirectional Long Short Term Memory

Full Text:

PDF


References

[1] M. Mounir , P. Karsmakers and T. V. Waterschoot, “Musical note onset detection based on a spectral sparsity measure,” EURASIP Journal on Audio, Speech, and Music Processing. 2021, Article no. 30, 2021 [Online]. Available: https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-021-00214-7 [Accessed 22-Nov-2022]

[2]. B. Stasiakand, J. Mońko, and A. Niewiadomski, “NOTE ONSET DETECTION IN MUSICAL SIGNALS VIANEURAL–NETWORK–BASED MULTI–ODF FUSION” Int. J. Appl. Math. Comput. Sci., 2016, Vol. 26, No. 1, 203–213].

[3] E. Benetos, S. Dixon, Z. Duan, S. Ewert, Automatic music transcription: an overview. IEEE Signal Process. Mag.36(1), 20–30 (2019).

[4] Risnandar., 2018, Pelarasan Gamelan Jawa.

[5] D. K. Sari, D. P. Wulandari. And Y. K. Suprapto, “Training Performance of Recurrent Neural Network using RTRL and BPTT for Gamelan Onset Detection”, International Conference on Electronics Representation and Algorithm (ICERA 2019)

[6] A. Rizal, R. Hidayat & H. A. Nugroho, “COMPARISON OF DISCRETE WAVELET TRANSFORM AND WAVELET PACKET DECOMPOSITION FOR THE LUNG SOUND CLASSIFICATION”, Far East Journal of Electronics and Communications, vol. 17, p.1065-1078, 2017

[7] B. Faghih, S. Chakraborty, A. Yaseen and J. Timoney, “A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments”, Appl. Sci., vol. 12, p.7391, 2022 [Online]. Available: https://www.mdpi.com/2076-3417/12/15/7391. [Accessed: 22 Nov 2022]

[8]. A. Schindler, T. Lidy and S. Böck, “Deep Learning for Music Information Retrieval”, 2018 [Online]. Available: https://github.com/slychief/ismir2018_tutorial, [Accessed 3-Aug-2022

[9] J. Zhang, Y. Zeng, B. Starly. “Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis”, SN Appl Sci, vol.3, p.442. 2021[Online]. Available: https://doi.org/10.1007/s42452-021-04427-5. [Accessed: 22 Nov 2022]

[10] Schindler, A., Lidy, T., & Böck, S., 2018, Deep Learning for Music Information Retrieval, https://github.com/slychief/ismir2018_tutorial, diakses pada 3 Agustus 2022

[11] J. Muradeli, “See-RNN”, 2019 [Online], Available: https://github.com/OverLordGoldDragon/see-rnn, [Accessed: 22 Nov 2022]



DOI: https://doi.org/10.22146/ijeis.79534

Article Metrics

Abstract views : 293 | views : 239

Refbacks

  • There are currently no refbacks.




Copyright (c) 2023 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis



View My Stats1
View My Stats2