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Song Recognition Based on Audio Finger printing
Authors
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India.
Article Information
- Published In
- Volume 5, Issue 4 (2025)
- Published Online
- July-August 2025
- Pages
- 34-38
- Article Type
- Original Article
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References
1. A. Wang, “An industrial strength audio search algorithm,” in Proc. 4th Int. Conf. Music Information Retrieval (ISMIR), Baltimore, MD, USA, 2003, pp. 7–13.
2. D. Ellis, “Robust landmark-based audio fingerprinting,” Columbia Univ., LabROSA, New York, NY, Tech. Rep., 2009.
3. D. P. W. Ellis and C. Cotton, “Matching pursuits with time-frequency dictionaries,” IEEE Signal Process. Mag., vol. 18, no. 6, pp. 52–68, Nov. 2001.
4. S. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Trans. Speech Audio Process., vol. 10, no. 5, pp. 293–302, Jul. 2002.
5. R. Lyon, “Machine hearing: An emerging field,” IEEE Signal Process. Mag., vol. 27, no. 5, pp. 131–139, Sept. 2010.
6. B. Logan, “Mel frequency cepstral coefficients for music modeling,” in Proc. Int. Symp. Music Information Retrieval, 2000.
7. T. Zhang and C. C. J. Kuo, “Audio content analysis for online audiovisual data segmentation and classification,” IEEE Trans. Speech Audio Process., vol. 9, no. 4, pp. 441–457, May 2001.
8. G. Tzanetakis, A. Ermolinskyi, and P. Cook, “Pitch histograms in audio and symbolic music information retrieval,” J. New Music Res., vol. 32, no. 2, pp. 143–152, Jun. 2003.
9. R. J. McKay and I. Fujinaga, “Musical genre classification: Is it worth pursuing and how can it be improved?” in Proc. 7th Int. Conf. Music Information Retrieval (ISMIR), 2006, pp. 101–106.
10. L. Lu, H.-J. Zhang, and H. Jiang, “Content analysis for audio classification and segmentation,” IEEE Trans. Speech Audio Process., vol. 10, no. 7, pp. 504–516, Oct. 2002.
11. H. Fastl and E. Zwicker, Psychoacoustics: Facts and Models, 3rd ed. Springer, 2007.
12. M. Casey et al., “Content-based music information retrieval: Current directions and future challenges,” Proc. IEEE, vol. 96, no. 4, pp. 668–696, Apr. 2008.
13. J. Salamon, J. P. Bello, “Deep convolutional neural networks and data augmentation for environmental sound classification,” IEEE Signal Process. Lett., vol. 24, no. 3, pp. 279–283, Mar. 2017.
14. J. Serra, M. Müller, P. Grosche, and J. L. Arcos, “Unsupervised music structure annotation by time series structure features and segment similarity,” IEEE Trans. Multimedia, vol. 16, no. 5, pp. 1229–1240, Aug. 2014.
15. D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, “Semantic annotation and retrieval of music and sound effects,” IEEE Trans. Audio Speech Lang. Process., vol. 16, no. 2, pp. 467–476, Feb. 2008.
16. A. Ghias, J. Logan, D. Chamberlin, and B. C. Smith, “Query by humming: Musical information retrieval in an audio database,” in Proc. ACM Int. Conf. Multimedia, 1995, pp. 231–236.
17. S. Dixon, “Evaluation of the audio beat tracking system BeatRoot,” J. New Music Res., vol. 36, no. 1, pp. 39–50, Mar. 2007.
18. X. Serra, “Musical sound modeling with sinusoidal plus residual and with pitch-synchronous overlap-add (PSOLA) techniques,” in Musical Signal Processing, CRC Press, 1997.
19. E. Scheirer and M. Slaney, “Construction and evaluation of a robust multifeature speech/music discriminator,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 1997, pp. 1331–1334.
20. J. L. Rouas, J. P. Laurent, and M. Rodet, “Automatic melody transcription using signal processing and statistical methods,” IEEE Trans. Audio Speech Lang. Process., vol. 14, no. 1, pp. 18–27, Jan. 2006.
21. S. Karras et al., “Training generative adversarial networks with limited data,” Proc. NeurIPS, vol. 33, pp. 12104–12114, 2020.
22. Y. Luo, J. Ren, and M. Liu, “Face aging and rejuvenation by conditional multi-adversarial autoencoder with ranking loss,” IEEE Access, vol. 8, pp. 127638–127651, 2020.
23. OpenCV, “Open Source Computer Vision Library,” [Online]. Available: https://opencv.org
24. Streamlit, “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io/
2. D. Ellis, “Robust landmark-based audio fingerprinting,” Columbia Univ., LabROSA, New York, NY, Tech. Rep., 2009.
3. D. P. W. Ellis and C. Cotton, “Matching pursuits with time-frequency dictionaries,” IEEE Signal Process. Mag., vol. 18, no. 6, pp. 52–68, Nov. 2001.
4. S. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Trans. Speech Audio Process., vol. 10, no. 5, pp. 293–302, Jul. 2002.
5. R. Lyon, “Machine hearing: An emerging field,” IEEE Signal Process. Mag., vol. 27, no. 5, pp. 131–139, Sept. 2010.
6. B. Logan, “Mel frequency cepstral coefficients for music modeling,” in Proc. Int. Symp. Music Information Retrieval, 2000.
7. T. Zhang and C. C. J. Kuo, “Audio content analysis for online audiovisual data segmentation and classification,” IEEE Trans. Speech Audio Process., vol. 9, no. 4, pp. 441–457, May 2001.
8. G. Tzanetakis, A. Ermolinskyi, and P. Cook, “Pitch histograms in audio and symbolic music information retrieval,” J. New Music Res., vol. 32, no. 2, pp. 143–152, Jun. 2003.
9. R. J. McKay and I. Fujinaga, “Musical genre classification: Is it worth pursuing and how can it be improved?” in Proc. 7th Int. Conf. Music Information Retrieval (ISMIR), 2006, pp. 101–106.
10. L. Lu, H.-J. Zhang, and H. Jiang, “Content analysis for audio classification and segmentation,” IEEE Trans. Speech Audio Process., vol. 10, no. 7, pp. 504–516, Oct. 2002.
11. H. Fastl and E. Zwicker, Psychoacoustics: Facts and Models, 3rd ed. Springer, 2007.
12. M. Casey et al., “Content-based music information retrieval: Current directions and future challenges,” Proc. IEEE, vol. 96, no. 4, pp. 668–696, Apr. 2008.
13. J. Salamon, J. P. Bello, “Deep convolutional neural networks and data augmentation for environmental sound classification,” IEEE Signal Process. Lett., vol. 24, no. 3, pp. 279–283, Mar. 2017.
14. J. Serra, M. Müller, P. Grosche, and J. L. Arcos, “Unsupervised music structure annotation by time series structure features and segment similarity,” IEEE Trans. Multimedia, vol. 16, no. 5, pp. 1229–1240, Aug. 2014.
15. D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, “Semantic annotation and retrieval of music and sound effects,” IEEE Trans. Audio Speech Lang. Process., vol. 16, no. 2, pp. 467–476, Feb. 2008.
16. A. Ghias, J. Logan, D. Chamberlin, and B. C. Smith, “Query by humming: Musical information retrieval in an audio database,” in Proc. ACM Int. Conf. Multimedia, 1995, pp. 231–236.
17. S. Dixon, “Evaluation of the audio beat tracking system BeatRoot,” J. New Music Res., vol. 36, no. 1, pp. 39–50, Mar. 2007.
18. X. Serra, “Musical sound modeling with sinusoidal plus residual and with pitch-synchronous overlap-add (PSOLA) techniques,” in Musical Signal Processing, CRC Press, 1997.
19. E. Scheirer and M. Slaney, “Construction and evaluation of a robust multifeature speech/music discriminator,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 1997, pp. 1331–1334.
20. J. L. Rouas, J. P. Laurent, and M. Rodet, “Automatic melody transcription using signal processing and statistical methods,” IEEE Trans. Audio Speech Lang. Process., vol. 14, no. 1, pp. 18–27, Jan. 2006.
21. S. Karras et al., “Training generative adversarial networks with limited data,” Proc. NeurIPS, vol. 33, pp. 12104–12114, 2020.
22. Y. Luo, J. Ren, and M. Liu, “Face aging and rejuvenation by conditional multi-adversarial autoencoder with ranking loss,” IEEE Access, vol. 8, pp. 127638–127651, 2020.
23. OpenCV, “Open Source Computer Vision Library,” [Online]. Available: https://opencv.org
24. Streamlit, “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io/
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