Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 52 tok/s
Gemini 2.5 Pro 55 tok/s Pro
GPT-5 Medium 25 tok/s Pro
GPT-5 High 26 tok/s Pro
GPT-4o 107 tok/s Pro
Kimi K2 216 tok/s Pro
GPT OSS 120B 468 tok/s Pro
Claude Sonnet 4 39 tok/s Pro
2000 character limit reached

Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis (2210.15356v3)

Published 27 Oct 2022 in cs.SD, cs.IR, and eess.AS

Abstract: Music Structure Analysis (MSA) consists of representing a song in sections (such as chorus'',verse'', ``solo'' etc), and can be seen as the retrieval of a simplified organization of the song. This work presents a new algorithm, called Convolutive Block-Matching (CBM) algorithm, devoted to MSA. In particular, the CBM algorithm is a dynamic programming algorithm, applying on autosimilarity matrices, a standard tool in MSA. In this work, autosimilarity matrices are computed from the feature representation of an audio signal, and time is sampled on the barscale. We study three different similarity functions for the computation of autosimilarity matrices. We report that the proposed algorithm achieves a level of performance competitive to that of supervised State-of-the-Art methods on 3 among 4 metrics, while being unsupervised.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (24)
  1. J. Paulus, M. Müller, and A. Klapuri, “State of the art report: Audio-based music structure analysis,” in International Society for Music Information Retrieval Conference (ISMIR), 2010, pp. 625–636.
  2. O. Nieto, G. J. Mysore, C.-I. Wang, J. B. Smith, J. Schlüter, T. Grill, and B. McFee, “Audio-based music structure analysis: Current trends, open challenges, and applications,” Transactions of the International Society for Music Information Retrieval, vol. 3, no. 1, 2020.
  3. A. Marmoret, J. Cohen, and F. Bimbot, “as_seg: module for computing and segmenting autosimilarity matrices,” 2022. [Online]. Available: https://gitlab.inria.fr/amarmore/autosimilarity˙segmentation
  4. M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, “RWC Music Database: Popular, Classical and Jazz Music Databases,” in International Society for Music Information Retrieval Conference (ISMIR), 2002, pp. 287–288.
  5. J. B. Smith, J. A. Burgoyne, I. Fujinaga, D. De Roure, and J. S. Downie, “Design and creation of a large-scale database of structural annotations,” in International Society for Music Information Retrieval Conference (ISMIR), 2011, pp. 555–560.
  6. J. Foote, “Automatic audio segmentation using a measure of audio novelty,” in IEEE International Conference on Multimedia and Expo. Proceedings Latest Advances in the Fast Changing World of Multimedia.   IEEE, 2000, pp. 452–455.
  7. K. Jensen, “Multiple scale music segmentation using rhythm, timbre, and harmony,” EURASIP Journal on Advances in Signal Processing, vol. 2007, pp. 1–11, 2006.
  8. G. Sargent, F. Bimbot, and E. Vincent, “Estimating the structural segmentation of popular music pieces under regularity constraints,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 2, pp. 344–358, 2016.
  9. B. McFee and D. Ellis, “Analyzing song structure with spectral clustering,” in International Society for Music Information Retrieval Conference (ISMIR), 2014, pp. 405–410.
  10. J. Serra, M. Müller, P. Grosche, and J. L. Arcos, “Unsupervised music structure annotation by time series structure features and segment similarity,” IEEE Transactions on Multimedia, vol. 16, no. 5, pp. 1229–1240, 2014.
  11. T. Grill and J. Schlüter, “Music boundary detection using neural networks on combined features and two-level annotations,” in International Society for Music Information Retrieval Conference (ISMIR), 2015, pp. 531–537.
  12. M. C. McCallum, “Unsupervised learning of deep features for music segmentation,” in 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2019, pp. 346–350.
  13. J.-C. Wang, J. B. Smith, W.-T. Lu, and X. Song, “Supervised metric learning for music structure feature,” in International Society for Music Information Retrieval Conference (ISMIR), 2021, pp. 730–737.
  14. J. Salamon, O. Nieto, and N. J. Bryan, “Deep embeddings and section fusion improve music segmentation,” in International Society for Music Information Retrieval Conference (ISMIR), 2021, pp. 594–601.
  15. M. Mauch, K. C. Noland, and S. Dixon, “Using musical structure to enhance automatic chord transcription,” in International Society for Music Information Retrieval Conference (ISMIR), 2009, pp. 231–236.
  16. M. Fuentes, B. McFee, H. C. Crayencour, S. Essid, and J. P. Bello, “A music structure informed downbeat tracking system using skip-chain conditional random fields and deep learning,” in 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2019, pp. 481–485.
  17. S. Böck, F. Korzeniowski, J. Schlüter, F. Krebs, and G. Widmer, “Madmom: A new python audio and music signal processing library,” in Proceedings of the 24th ACM international conference on Multimedia, 2016, pp. 1174–1178.
  18. A. Marmoret, J. E. Cohen, and F. Bimbot, “Barwise compression schemes for audio-based music structure analysis,” in 19th Sound and Music Computing Conference, SMC 2022.   Sound and music Computing network, 2022.
  19. B. McFee et al., “librosa/librosa: 0.8.1rc2.”   Zenodo, May 2021. [Online]. Available: https://doi.org/10.5281/zenodo.4792298
  20. R. Bellman, “On the theory of dynamic programming,” Proceedings of the national Academy of Sciences, vol. 38, no. 8, pp. 716–719, 1952.
  21. A. Marmoret, J. E. Cohen, N. Bertin, and F. Bimbot, “Uncovering audio patterns in music with nonnegative tucker decomposition for structural segmentation,” in International Society for Music Information Retrieval Conference (ISMIR), 2020, pp. 788–794.
  22. C. Raffel, B. McFee, E. J. Humphrey, J. Salamon, O. Nieto, D. Liang, D. P. W. Ellis, and Raffel, “mir_eval: A transparent implementation of common MIR metrics,” in International Society for Music Information Retrieval Conference (ISMIR), 2014, pp. 367–372.
  23. O. Nieto and J. P. Bello, “Systematic exploration of computational music structure research,” in International Society for Music Information Retrieval Conference (ISMIR), 2016, pp. 547–553.
  24. Y. Shiu, H. Jeong, and C.-C. J. Kuo, “Similarity matrix processing for music structure analysis,” in Proceedings of the 1st ACM workshop on Audio and music computing multimedia, 2006, pp. 69–76.
Citations (1)
View on Semantic Scholar
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube