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Evaluating Feature Selection Algorithms for Machine Learning-Based Musical Instrument Identification in Monophonic Recordings

Yıl 2024, Cilt: 7 Sayı: 2, 289 - 301, 31.08.2024

İsmet Emre Yücel , Ulaş Yurtsever

https://doi.org/10.35377/saucis...1516717

Öz

Musical instrument identification (MII) research has been studied as a subfield of the Music Information Retrieval (MIR) field. Conventional MII models are developed based on hierarchical models representing musical instrument families. However, for MII models to be used in the field of music production, they should be developed based on the arrangement-based functions of instruments in musical styles rather than these hierarchical models. This study investigates how the performance of machine learning based classification algorithms for Guitar, Bass guitar and Drum classes changes with different feature selection algorithms, considering a popular music production scenario. To determine the effect of feature statistics on model performance, Minimum Redundancy Maximum Relevance (mRMR), Chi-sqaure (Chi2), ReliefF, Analysis of Variance (ANOVA) and Kruskal Wallis feature selection algorithms were used. In the end, the neural network algorithm with wide hyperparameters (WNN) achieved the best classification accuracy (91.4%) when using the first 20 statistics suggested by the mRMR and ReliefF feature selection algorithms.

Anahtar Kelimeler

Musical instrument identification Audio features Feature selection Machine learning

Kaynakça

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Yıl 2024, Cilt: 7 Sayı: 2, 289 - 301, 31.08.2024

İsmet Emre Yücel , Ulaş Yurtsever

https://doi.org/10.35377/saucis...1516717

Öz

Kaynakça

  • [1] A. Ghosh, A. Pal, D. Sil, and S. Palit, “Music Instrument Identification Based on a 2-D Representation,” in 3rd International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques, ICEECCOT 2018, Institute of Electrical and Electronics Engineers Inc., Dec. 2018, pp. 509–513. doi: 10.1109/ICEECCOT43722.2018.9001486.
  • [2] U. Shukla, U. Tiwari, V. Chawla, and S. Tiwari, “Instrument classification using image based transfer learning,” in Proceedings of the 2020 International Conference on Computing, Communication and Security, ICCCS 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICCCS49678.2020.9277366.
  • [3] I. Kaminskyj and A. Materka, “AUTOMATIC SOURCE IDENTIFICATION OF MONOPHONIC MUSICAL INSTRUMENT SOUNDS,” Proceedings of the Australian and New Zealand Conference on Intelligent Information Systems, 1995.
  • [4] I. Kaminskyj and P. Voumard, “Enhanced automatic source identification of monophonic musical instrument sounds,” Proceedings of the Australian and New Zealand Conference on Intelligent Information Systems, no. November, pp. 76–79, 1996.
  • [5] K. D. Martin and Y. E. Kim, “2pMU9. Musical instrument identification: A pattern-recognition approach *,” in Presented at the 136th meeting of the Acoustical Society of America, Newyork, 1998.
  • [6] P. Herrera-Boyer, G. Peeters, and S. Dubnov, “Automatic classification of musical instrument sounds,” in International Journal of Phytoremediation, Journal of New Music Research, 2003, pp. 3–21.
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  • [9] Y. Han, J. Kim, and K. Lee, “Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music,” IEEE/ACM Trans Audio Speech Lang Process, vol. 25, no. 1, pp. 208–221, Jan. 2017, doi: 10.1109/TASLP.2016.2632307.
  • [10] T. Kitahara, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, “Instrument identification in polyphonic music: Feature weighting with mixed sounds, pitch-dependent timbre modeling, and use of musical context,” ISMIR 2005 - 6th International Conference on Music Information Retrieval, no. January, pp. 558–563, 2005.
  • [11] S. Chang, S. Member, T. Sikora, S. Member, and A. Puri, “Overview of the MPEG-7 Standard,” vol. 11, no. 6, pp. 688–695, 2001.
  • [12] M. R. Bai, A. Member, and C. Chen, “Intelligent Preprocessing and Classification of Audio Signals*.”
  • [13] P. Wei, F. He, L. Li, and J. Li, “Research on sound classification based on SVM,” Neural Comput Appl, vol. 32, no. 6, pp. 1593–1607, Mar. 2020, doi: 10.1007/s00521-019-04182-0.
  • [14] F. Alías, J. C. Socoró, and X. Sevillano, “A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds,” Applied Sciences, vol. 6, no. 5. Balkan Society of Geometers, 2016. doi: 10.3390/app6050143.
  • [15] J. D. Deng, C. Simmermacher, and S. Cranefield, “A study on feature analysis for musical instrument classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 38, no. 2, pp. 429–438, 2008, doi: 10.1109/TSMCB.2007.913394.
  • [16] A. Aljanaki and M. Soleymani, “A data-driven approach to mid-level perceptual musical feature modeling,” Jun. 2018, [Online]. Available: http://arxiv.org/abs/1806.04903
  • [17] J. L. Fernández-Martínez and Z. Fernández-Muñiz, “The curse of dimensionality in inverse problems,” J Comput Appl Math, vol. 369, 2020, doi: 10.1016/j.cam.2019.112571.
  • [18] J. Osmalskyj, M. Van Droogenbroeck, and J. J. Embrechts, “Performances of low-level audio classifiers for large-scale music similarity,” in International Conference on Systems, Signals, and Image Processing, 2014, pp. 91–94.
  • [19] Z. Fu, G. Lu, K. M. Ting, and D. Zhang, “On feature combination for music classification,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, pp. 453–462. doi: 10.1007/978-3-642-14980-1_44.
  • [20] M. Chmulik, R. Jarina, M. Kuba, and E. Lieskovska, “Continuous music emotion recognition using selected audio features,” in 2019 42nd International Conference on Telecommunications and Signal Processing, TSP 2019, 2019. doi: 10.1109/TSP.2019.8768806.
  • [21] J. Grekow, “Audio features dedicated to the detection of arousal and valence in music recordings,” in Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017, 2017, pp. 40–44. doi: 10.1109/INISTA.2017.8001129.
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  • [23] M. Liu and C. Wan, “Feature selection for automatic classification of musical instrument sounds,” Proceedings of the ACM International Conference on Digital Libraries, pp. 247–248, 2001, doi: 10.1145/379437.379663.
  • [24] S. R. Gulhane, S. S. Badhe, and S. D. Shirbahadurkar, “Cepstral (MFCC) Feature and Spectral (Timbral) Features Analysis for Musical Instrument Sounds,” Proceedings - 2018 IEEE Global Conference on Wireless Computing and Networking, GCWCN 2018, pp. 109–113, 2018, doi: 10.1109/GCWCN.2018.8668628.
  • [25] P. S. Jadhav, “Classification of Musical Instruments sounds by Using MFCC and Timbral Audio Audio Descriptors,” 2015.
  • [26] J. Lee, T. Kim, J. Park, and J. Nam, “Raw Waveform-based Audio Classification Using Sample-level CNN Architectures,” no. Nips, 2017.
  • [27] K. Avramidis, A. Kratimenos, C. Garoufis, A. Zlatintsi, and P. Maragos, “Deep convolutional and recurrent networks for polyphonic instrument classification from monophonic raw audio waveforms,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2021-June, pp. 3010–3014, 2021, doi: 10.1109/ICASSP39728.2021.9413479.
  • [28] T. M. Hehn, J. F. P. Kooij, and F. A. Hamprecht, “End-to-End Learning of Decision Trees and Forests,” Int J Comput Vis, vol. 128, no. 4, 2020, doi: 10.1007/s11263-019-01237-6.
  • [29] Z. ÇETİNKAYA and F. HORASAN, “Decision Trees in Large Data Sets,” Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi, vol. 13, no. 1, 2021, doi: 10.29137/umagd.763490.
  • [30] A. Araveeporn, “Comparison of Logistic Regression and Discriminant Analysis for Classification of Multicollinearity Data,” WSEAS Trans Math, vol. 22, 2023, doi: 10.37394/23206.2023.22.15.
  • [31] A. Saini, “Guide on Support Vector Machine (SVM) Algorithm,” Analytics Vidhya, 2024.
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Toplam 72 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

İsmet Emre Yücel 0000-0001-7018-3349

Ulaş Yurtsever 0000-0003-3438-6872

Erken Görünüm Tarihi 27 Ağustos 2024
Yayımlanma Tarihi 31 Ağustos 2024
Gönderilme Tarihi 15 Temmuz 2024
Kabul Tarihi 21 Ağustos 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 2

Kaynak Göster

IEEE İ. E. Yücel ve U. Yurtsever, “Evaluating Feature Selection Algorithms for Machine Learning-Based Musical Instrument Identification in Monophonic Recordings”, SAUCIS, c. 7, sy. 2, ss. 289–301, 2024, doi: 10.35377/saucis...1516717.

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