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Machine learning based automatic melody transposition using index addition technique
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AI
DOI：10.1007/s00521-026-12303-9.png)
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Abstract
En 中文
Music transposition is a challenging topic of interest to both musicians and researchers. In musical analysis involving chord recognition, automatic chord prediction and generation, transposition of a melody to C major or C minor is desired. In addition to manual transposition of music, algorithmic transposition involving machine learning techniques has garnered a lot of attention nowadays. In this article, an algorithm that successfully transposes a given tune into the key of C major or C minor by employing the index addition approach and support vector machine is being proposed. For our experiments, we created a dataset derived from a few renowned datasets. Our system consists of two modules: one for key estimation and the other for transposition. The key estimation system is based on support vector machine, and transposition is done using a novel algorithm based on index addition technique. With an accuracy of 91.5%, both XG boost and Support Vector Machine (SVM) outperforms all other machine learning algorithms and we took SVM into consideration for key estimation based on better generalization, simpler structure, and more reliable margin-based decision making. For systematic evaluation, we have considered well known sample melodies. It is revealed that the higher accuracy of SVM technique has enabled the index addition technique to accurately transpose the given song to key of C. Although the performance of our algorithm appears to be comparable to other methods, our system proves to be simple and novel in that it requires only a single step to transpose songs in either major or minor keys, whereas certain other systems require two separate techniques for transposing major and minor keys.
Keywords:
Pitch class profile
Pitch estimation
Transposition
Music information retrieval
Musical instrument digital interfaces
Support vector machines
Journal
IF:
4.5
Papers:
800
Citations:
3.2W
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