journal article·2026-01-30Open access

Tonal Mode Detection Based on the Triple Composite Signature of Fifths

Tomasz Łukaszewicz, Zuzanna Kania, Dariusz Kania

Applied Sciences· Volume 16· Issue 3

Abstract

In this article, a novel algorithm for tonal mode classification utilizing the signature of fifths is presented. The proposed method is an extension of previously developed algorithms used for determining the key and key signature within the Western tonal system. The core idea of the algorithm is to analyze the entire piece as well as its most important excerpts, namely, the beginning and the end. The analysis of these three samples (beginning, end, and entire piece) involves the calculation of their corresponding signatures of fifths, which constitute two-dimensional sets of vectors inscribed in the circle of fifths. The length of each vector comprising the signature of fifths corresponds to the multiplicity of notes associated with a particular pitch class, while its direction is related to the position of that pitch class with respect to the circle of fifths. Based on the obtained signatures of fifths, the so-called Triple Composite Signature of Fifths is created, for which both the main directed axis and the major/minor mode axis are determined. Tonal mode is then established heuristically, based on the values of angles between the characteristic vectors of each sample’s signature of fifths and the major/minor mode axis of the Triple Composite Signature of Fifths. The process of tonal mode classification involves defining a tonal mode indicator, which assumes the values of 1 for major and −1 for minor tonality. To evaluate the effectiveness of the developed algorithm, a series of experiments was carried out using datasets of works from the Romantic period by F. Chopin and F. Schubert, as well as piano compositions from various eras. The results confirmed the high effectiveness of the proposed method in identifying tonal mode.
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Research context

OpenAlex topics: Music and Audio Processing · Neuroscience and Music Perception · Music Technology and Sound Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland

Faculty of Management, University of Economics in Katowice, 40-287 Katowice, Poland

References (55)

Show source-provided references
1. A Multi-Level Tonal Interval Space for Modelling Pitch Relatedness and Musical Dissonance10.1080/09298215.2016.1182192 ↗
2. Bernardes, G., Davies, M., and Guedes, C. (2017, January 5–9). Automatic musical key estimation with mode bias. Proceedings of the International Conference on Acoustics Speech and Signal Processing (ICASSP), New Orleans, LA, USA.10.1109/ICASSP.2017.7952169 ↗
3. Harte, C., Sandler, M., and Gasser, M. (2006, January 27). Detecting harmonic change in musical audio. Proceedings of the Special 1st ACM Workshop on Audio and Music Computing Multimedia, Santa Barbara, CA, USA.10.1145/1178723.1178727 ↗
4. Corpus-derived key profiles are not transpositionally equivalent10.1525/mp.2017.34.5.531 ↗
5. Measuring and modeling real-time responses to music: The dynamics of tonality induction10.1068/p3312 ↗
6. Chew, E. (2000). Towards a Mathematical Model of Tonality. [Ph.D. Thesis, Massachusetts Institute of Technology].
7. Chen, T.-P., and Su, L. (2018, January 23–27). Functional harmony recognition of symbolic music data with multi-task recurrent neural networks. Proceedings of the 19th ISMIR Conference, Paris, France.
8. An information theoretic approach to chord categorization and functional harmony10.1080/09298215.2015.1036888 ↗
9. Hori, T., Nakamura, K., and Sagayama, S. (2017, January 12–15). Music chord recognition from audio data using bidirectional encoder-decoder LSTMs. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.10.1109/APSIPA.2017.8282235 ↗
10. A novel holistic modeling approach for generalized sound recognition10.1109/LSP.2013.2237902 ↗
11. Peiszer, E., Lidy, T., and Rauber, A. (2008, January 21). Automatic audio segmentation: Segment boundary and structure detection in popular music. Proceedings of the International Workshop on Learning the Semantics of Audio Signals, Paris, France.
12. Improving Music Genre Classification Using Automatically Induced Harmony Rules10.1080/09298215.2010.525654 ↗
13. Genre classification of music by tonal harmony10.3233/IDA-2010-0437 ↗
14. Chapin, H., Jantzen, K., Kelso, J.S., Steinberg, F., and Large, E. (2020). Dynamic Emotional and Neural Responses to Music Depend on Performance Expression and Listener Experience. PLoS ONE, 5.10.1371/journal.pone.0013812 ↗
15. Musical performance analysis in terms of emotions it evokes10.1007/s10844-018-0510-y ↗
16. Automatic melody composition based on a probabilistic model of music style and harmonic rules10.1016/j.knosys.2014.08.018 ↗
17. Examining emotion perception agreement in live music performance10.1109/TAFFC.2021.3093787 ↗
18. Letter to a musical friend
19. Second letter to a musical friend
20. Geometrical approximations to the structure of musical pitch10.1037/0033-295X.89.4.305 ↗
21. Out of the Grid and Into the Spiral: Geometric Interpretations of and Comparisons with the Spiral-Array Model
22. Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P. (2013, January 4–8). Audio chord recognition with recurrent neural networks. Proceedings of the 14th International Conference on Music Information Retrieval (ISMIR), Curitiba, Brazil.
23. Huang, C.-Z.A., Duvenaud, D., and Gajos, K.Z. (2016, January 7–10). Chordripple: Recommending chords to help novice composers go beyond the ordinary. Proceedings of the 21st International Conference on Intelligent User Interfaces, Sonoma, CA, USA.10.1145/2856767.2856792 ↗
24. Sabathé, R., Coutinho, E., and Schuller, B. (2017, January 14–19). Deep recurrent music writer: Memory-enhanced variational autoencoder-based musical score composition and an objective measure. Proceedings of the Neural Networks, 2017 International Joint Conference on Neural Networks, Anchorage, AK, USA.10.1109/IJCNN.2017.7966292 ↗
25. Mauch, M., and Dixon, S. (2010, January 9–13). Approximate note transcription for the improved identification of difficult chords. Proceedings of the 11th International Society for Music Information Retrieval Conference, Utrecht, The Netherlands.
26. Osmalskyj, J., Embrechts, J.-J., Piérard, S., and Van Droogenbroeck, M. (2012, January 9–11). Neural networks for musical chords recognition. Proceedings of the Actes des Journées d’Informatique Musicale (JIM 2012), Mons, Belgium.
27. Sigtia, S., Boulanger-Lewandowski, N., and Dixon, S. (2015, January 26–30). Audio chord recognition with a hybrid recurrent neural network. Proceedings of the 16th International Society for Music Information Retrieval Conference, Malaga, Spain.
28. Chuan, C.-H., and Chew, E. (2005). Polyphonic Audio Key Finding Using the Spiral Array CEG Algorithm. Proceedings of the IEEE International Conference on Multimedia and Expo, ICME 2005, Amsterdam, The Netherlands, 6 July 2005, IEEE.
29. Audio Key Finding: Considerations in System Design and Case Studies on Chopin’s 24 Preludes10.1155/2007/56561 ↗
30. Functional harmonic analysis using probabilistic models10.1162/0148926041790676 ↗
31. Dawson, M. (2018). Connectionist Representations of Tonal Music: Discovering Musical Patterns by Interpreting Artificial Neural Networks, AU Press, Athabasca University.10.15215/aupress/9781771992206.01 ↗
32. Deng, J., and Kwok, Y.-K. (2017). Large vocabulary automatic chord estimation using deep neural nets: Design framework, system variations and limitations. arXiv.
33. Korzeniowski, F., and Widmer, G. (September, January 28). End-to-end musical key estimation using a convolutional neural network. Proceedings of the 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.
34. Masada, K., and Bunescu, R. (2017, January 23–27). Chord recognition in symbolic music using semi-markov conditional random fields. Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China.
35. McFee, B., and Bello, J.P. (2017, January 23–27). Structured training for large-vocabulary chord recognition. Proceedings of the 18th International Conference on Music Information Retrieval (ISMIR), Suzhou, China.
36. Wu, Y., and Li, W. (2018, January 15–20). Music Chord Recognition Based on Midi-Trained Deep Feature and BLSTM-CRF Hybrid Decoding. Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.10.1109/ICASSP.2018.8461439 ↗
37. Zhou, X.-H., and Lerch, A. (2015, January 26–30). Chord Detection Using Deep Learning. Proceedings of the SIMIR 2015, Malaga, Spain.
38. MorpheuS: Generating structured music with constrained patterns and tension10.1109/TAFFC.2017.2737984 ↗
39. A key-finding algorithm based on music signature10.24425/aoa.2019.129260 ↗
40. Tracing the dynamic changes in perceived tonal organization in a spatial representation of musical keys10.1037/0033-295X.89.4.334 ↗
41. Krumhansl, C.L. (1990). Cognitive Foundations of Musical Pitch, Oxford University Press.
42. Pitch-Class Distribution and Key Identification10.1525/mp.2008.25.3.193 ↗
43. Bayesian models of musical structure and cognition10.1177/102986490400800204 ↗
44. Aarden, B.J. (2003). Dynamic Melodic Expectancy. [Ph.D. Thesis, The Ohio State University].
45. The Use of Large Corpora to Train a New Type of Key-Finding Algorithm: An Improved Treatment of the Minor Mode10.1525/mp.2013.31.1.59 ↗
46. Bellmann, H. (2006). About the determination of key of a musical excerpt. International Symposium on Computer Music Modeling and Retrieval (CMMR 2006), Springer.10.1007/11751069_7 ↗
47. A Real-Time Key-Finding Algorithm Based on the Signature of Fifths10.24425/aoa.2024.148817 ↗
48. Duan, Z., Lu, L., and Zhang, C. (April, January 23). Audio tonality mode classification without tonic annotations. Proceedings of the IEEE International Conference on Multimedia and Expo, Hannover, Germany.
49. Trajectory of fifths in tonal mode detection10.1109/ACCESS.2025.3584400 ↗
50. Trajectory of fifths in music data mining10.1109/ACCESS.2021.3049266 ↗
51. A Music Classification Approach Based on the Trajectory of Fifths10.1109/ACCESS.2022.3190016 ↗
52. (2025, October 01). Schubert Winterreise Dataset. Available online: https://zenodo.org/record/4122060#.YvY4YXZByUk.
53. Müller, M., Konz, V., Arifi-Müller, V., Bogler, W., Zeitler, J., and Group Semantic Audio Processing (2024, January 10–14). Saarland Music Data: MIDI-Audio Piano Music (v2) [Data set]. Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR), Miami, FL, USA.
54. Automatic Key Detection in Audio Recordings Using the Signature of Fifths10.1109/ACCESS.2025.3609536 ↗
55. Trajectory of Fifths Based on Chroma Subbands Extraction—A New Approach to Music Representation, Analysis, and Classification