Two new research papers have been produced at CNMAT during the past semester.


  • Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval, by H. C. Crayencour and C. E. Cella, Frontiers of Digital Humanities (journal), April 2019
    • ABSTRACT: Within the last 15 years, the field of Music Information Retrieval (MIR) has made tremendous progress in the development of algorithms for organizing and analyzing the ever-increasing large and varied amount of music and music-related data available digitally. However, the development of content-based methods to enable or ameliorate multimedia retrieval still remains a central challenge. In this perspective paper, we critically look at the problem of automatic chord estimation from audio recordings as a case study of content-based algorithms, and point out several bottlenecks in current approaches: expressiveness and flexibility are obtained to the expense of robustness and vice versa; available multimodal sources of information are little exploited; modeling multi-faceted and strongly interrelated musical information is limited with current architectures; models are typically restricted to short-term analysis that does not account for the hierarchical temporal structure of musical signals. Dealing with music data requires the ability to tackle both uncertainty and complex relational structure at multiple levels of representation. Traditional approaches have generally treated these two aspects separately, probability and learning being the usual way to represent uncertainty in knowledge, while logical representation being the usual way to represent knowledge and complex relational information. We advocate that the identified hurdles of current approaches could be overcome by recent developments in the area of Statistical Relational Artificial Intelligence (StarAI) that unifies probability, logic and (deep) learning. We show that existing approaches used in MIR find powerful extensions and unifications in StarAI, and we explain why we think it is time to consider the new perspectives offered by this promising research field.


  • ​Estimating unobserved audio features for targed-based orchestration, by J. Gillick, C. E. Cella and D. Bamman, ISMIR 2019
    • ABSTRACT: Target-based assisted orchestration can be thought of as the process of searching for optimal combinations of sounds to match a target sound, given a database of samples, a similarity metric, and a set of constraints. A typical solution to this problem is a proposed orchestral score where candidates are ranked by similarity in some feature space be- tween the target sound and the mixture of audio samples in the database corresponding to the notes in the score; in the orchestral setting, valid scores may contain dozens of instruments sounding simultaneously.

      Generally, target-based assisted orchestration systems consist of a combinatorial optimization algorithm and a constraint solver that are jointly optimized to find valid solutions. A key step in the optimization involves gener- ating a large number of combinations of sounds from the database and then comparing the features of each mixture of sounds with the target sound. Because of the high computational cost required to synthesize a new audio file and then compute features for every combination of sounds, in practice, existing systems instead estimate the features of each new mixture using precomputed features of the indi- vidual source files making up the combination. Currently, state-of-the-art systems use a simple linear combination to make these predictions, even if the features in use are not themselves linear.

    • In this work, we explore neural models for estimating the features of a mixture of sounds from the features of the component sounds, finding that standard features can be estimated with accuracy significantly better than that of the methods currently used in assisted orchestration systems. We present quantitative comparisons and discuss the implications of our findings for target-based orchestration problems.