2022

1. Ph.D. Thesis

   Unsupervised machine learning paradigms for the representation of music similarity and structure

   Axel Marmoret^*

   Université de Rennes. Segmentation code can be found here, low-rank factorization code can be found here, and the specific methods for compressing music barwise can be found here , Dec 2022

   Abs Bib PDF Video

   Musical structure, defined as a simplified representation of the organization of a song, is an important musicological concept, but hard to automatically estimate. This thesis presents new methods to automatically estimate the structural segmentation of a song, focusing the study of music at the barscale. By developing a new segmentation algorithm (called “CBM”) and by comparing several unsupervised compression schemes (from linear and multilinear algebra to neural networks), paradigms introduced in this thesis result in segmentation performance outperforming those of the unsupervised State-of-the-Art methods and almost similar with those of the global State-of-the-Art, obtained with supervised machine learning algorithms. In particular, as the methods described in this thesis are unsupervised, the estimation do not rely on annotated data, lowering the bias in the estimates related to ambiguity and subjectivity (inherent to musical structure) while limiting the loss in performance compared to the best supervised methods. In addition, some of the methods studied in this thesis (in particular Nonnegative Tucker Decomposition) allow to extract automatically interpretable parts of a song which may be used for other task than the estimation of structure, and participate in the development of interpretable machine and deep learning algorithms, which is a major field of research nowadays.

   @phdthesis{marmoret2022unsupervised,
     title = {Unsupervised machine learning paradigms for the representation of music similarity and structure},
     author = {Marmoret, Axel},
     year = {2022},
     month = dec,
     school = {Universit{\'e} de Rennes},
     url = {https://hal.science/tel-03937846},
   }

2. International Conf.

   Barwise Compression Schemes for Audio-Based Music Structure Analysis

   Axel Marmoret^*, Jérémy E Cohen^†, and Frédéric Bimbot^*

   In Proceedings of the 19th Sound and Music Computing Conference, 2022

   Abs DOI Bib PDF Code Poster

   Music Structure Analysis (MSA) consists in segmenting a music piece in several distinct sections. We approach MSA within a compression framework, under the hypothesis that the structure is more easily revealed by a simplified representation of the original content of the song. More specifically, under the hypothesis that MSA is correlated with similarities occurring at the bar scale, this article introduces the use of linear and non-linear compression schemes on barwise audio signals. Compressed representations capture the most salient components of the different bars in the song and are then used to infer the song structure using a dynamic programming algorithm. This work explores both low-rank approximation models such as Principal Component Analysis or Nonnegative Matrix Factorization and “piece-specific” Auto-Encoding Neural Networks, with the objective to learn latent representations specific to a given song. Such approaches do not rely on supervision nor annotations, which are well-known to be tedious to collect and possibly ambiguous in MSA description. In our experiments, several unsupervised compression schemes achieve a level of performance comparable to that of state-of-the-art supervised methods (for 3s tolerance) on the RWC-Pop dataset, showcasing the importance of the barwise compression processing for MSA.

   @inproceedings{marmoret2022barwise,
     title = {Barwise Compression Schemes for Audio-Based Music Structure Analysis},
     author = {Marmoret, Axel and Cohen, J{\'e}r{\'e}my E and Bimbot, Fr{\'e}d{\'e}ric},
     booktitle = {{Proceedings of the 19th Sound and Music Computing Conference}},
     year = {2022},
     month = {},
     doi = {10.5281/zenodo.6798330},
     url = {https://hal.science/hal-03600873},
   }