Artificial Neural Networks for Downbeat Estimation and Varying Tempo Induction in Music Signals

Author:

Nadi SarahORCID,Yao JianguoORCID

Publisher

Springer Nature Singapore

Reference36 articles.

1. Benetos, E., Dixon, S., Duan, Z., Ewert, S.: Automatic music transcription: an overview. IEEE Signal Process. Mag. 36(1), 20–30 (2018)

2. Böck, S., Davies, M.E.: Deconstruct, analyse, reconstruct: how to improve tempo, beat, and downbeat estimation. In: Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR), Montreal, QC, Canada, pp. 12–16 (2020)

3. Chiu, C.Y., Su, A.W.Y., Yang, Y.H.: Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking. IEEE Signal Process. Lett. 28, 1100–1104 (2021)

4. 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)

5. Davies, M.E., Degara, N., Plumbley, M.D.: Evaluation methods for musical audio beat tracking algorithms. Queen Mary University of London, Centre for Digital Music, Technical report C4DM-TR-09-06 (2009)

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