Abstract
Notation of a music piece is an extremely important resource for musicians. It requires mastery and experience to transcribe a piece accurately which lays the path for automatic music transcription systems to help budding musicians. A piece can be divided into two parts namely the background music (BGM) and lead melody. The BGM is an extremely important aspect of a piece. It is responsible for setting the mood of a composition and at the same time makes it complete. There are different musical instruments which are used in a composition both in the BGM and lead sections one of them being the bass guitar. It bonds with the percussion instruments to form the spinal cord of a piece. It is very much important to transcribe the bass section of a composition for understanding as well as performance. Prior to identification of the notes being played, it is essential to distinguish the different patterns/grooves. In this paper, a system is presented to differentiate bass grooves. Tests were carried out with 60K clips and a best accuracy of 98.46% was obtained.
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References
Abeßer, J.: Automatic string detection for bass guitar and electric guitar. In: Aramaki, M., Barthet, M., Kronland-Martinet, R., Ystad, S. (eds.) CMMR 2012. LNCS, vol. 7900, pp. 333–352. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41248-6_18
Kroher, N., Gómez, E.: Automatic transcription of flamenco singing from polyphonic music recordings. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 24(5), 901–913 (2016)
Wats, N., Patra, S.: Automatic music transcription using accelerated multiplicative update for non-negative spectrogram factorization. In: 2017 International Conference on Intelligent Computing and Control (I2C2), pp. 1–5. IEEE (2017)
Wu, C.W., et al.: A review of automatic drum transcription. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 26(9), 1457–1483 (2018)
Avci, K., Acuner, T.Ş.: Automatic transcription of open string notes from violin recordings. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. IEEE (2017)
Puri, S.B., Mahajan, S.P.: Review on automatic music transcription system. In: 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1–6. IEEE (2017)
Cogliati, A., Duan, Z., Wohlberg, B.: Context-dependent piano music transcription with convolutional sparse coding. IEEE/ACM Trans. Audio Speech Lang. Process. 24(12), 2218–2230 (2016)
Stein, M., Abeber, J., Dittmar, C., Schuller, G.: Automatic detection of audio effects in guitar and bass recordings. In: Audio Engineering Society Convention 128. Audio Engineering Society (2010)
Mukherjee, H., et al.: An ensemble learning-based language identification system. In: Maharatna, K., Kanjilal, M.R., Konar, S.C., Nandi, S., Das, K. (eds.) Computational Advancement in Communication Circuits and Systems. LNEE, vol. 575, pp. 129–138. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8687-9_12
Thelwall, M.: The precision of the arithmetic mean, geometric mean and percentiles for citation data: an experimental simulation modelling approach. J. Informetr. 10(1), 110–123 (2016)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Acknowledgment
The authors would like to thank Mr. Pradip Ghosh and Mr. Subho Dey for their help with the musical technicalities and data collection. They also thank WWW.PresentationGO.com for the block diagram template.
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Mukherjee, H., Dhar, A., Obaidullah, S.M., Santosh, K.C., Phadikar, S., Roy, K. (2021). Segregating Bass Grooves from Audio: A Rotation Forest-Based Approach. In: Santosh, K.C., Gawali, B. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1381. Springer, Singapore. https://doi.org/10.1007/978-981-16-0493-5_32
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DOI: https://doi.org/10.1007/978-981-16-0493-5_32
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