Measuring the Structural Complexity of Music: From Structural Segmentations to the Automatic Evaluation of Models for Music Generation. De Berardinis, J., Cangelosi, A., & Coutinho, E. IEEE/ACM Transactions on Audio Speech and Language Processing, 30:1963-1976, 2022. ![Measuring the Structural Complexity of Music: From Structural Segmentations to the Automatic Evaluation of Models for Music Generation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Composing musical ideas longer than motifs or figures is still rare in music generated by machine learning methods, a problem that is commonly referred to as the lack of long-term structure in the generated sequences. In addition, the evaluation of the structural complexity of artificial compositions is still a manual task, requiring expert knowledge, time and involving subjectivity which is inherent in the perception of musical structure. Based on recent advancements in music structure analysis, we automate the evaluation process by introducing a collection of metrics that can objectively describe structural properties of the music signal. This is done by segmenting music hierarchically, and computing our metrics on the resulting hierarchies to characterise the decomposition process of music into its structural components. We tested our method on a dataset collecting music with different degrees of structural complexity, from random and computer-generated pieces to real compositions of different genres and formats. Results indicate that our method can discriminate between these classes of complexity and identify further non-trivial subdivisions according to their structural properties. Our work contributes a simple yet effective framework for the evaluation of music generation models in regard to their ability to create structurally meaningful compositions.
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title = {Measuring the Structural Complexity of Music: From Structural Segmentations to the Automatic Evaluation of Models for Music Generation},
type = {article},
year = {2022},
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pages = {1963-1976},
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abstract = {Composing musical ideas longer than motifs or figures is still rare in music generated by machine learning methods, a problem that is commonly referred to as the lack of long-term structure in the generated sequences. In addition, the evaluation of the structural complexity of artificial compositions is still a manual task, requiring expert knowledge, time and involving subjectivity which is inherent in the perception of musical structure. Based on recent advancements in music structure analysis, we automate the evaluation process by introducing a collection of metrics that can objectively describe structural properties of the music signal. This is done by segmenting music hierarchically, and computing our metrics on the resulting hierarchies to characterise the decomposition process of music into its structural components. We tested our method on a dataset collecting music with different degrees of structural complexity, from random and computer-generated pieces to real compositions of different genres and formats. Results indicate that our method can discriminate between these classes of complexity and identify further non-trivial subdivisions according to their structural properties. Our work contributes a simple yet effective framework for the evaluation of music generation models in regard to their ability to create structurally meaningful compositions.},
bibtype = {article},
author = {De Berardinis, Jacopo and Cangelosi, Angelo and Coutinho, Eduardo},
doi = {10.1109/TASLP.2022.3178203},
journal = {IEEE/ACM Transactions on Audio Speech and Language Processing}
}
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