15 ESRs were trained in the field of MIR and their research address the main challenges that face the field. These 3 challenges were identified as relating to data-driven, knowledge driven and user-driven aspects.
Each aspect has been addressed on an individual basis, by each researcher via their research project. All 15 ESRs' PhD topics reflected the importance that data, knowledge, and user driven methodologies have in the field and are a good example of the diverse approaches that are currently being proposed.
Many of the PhD research projects involved the development and use of large datasets, the development and use of deep-learning methodologies, and they all faced the challenge that music-specific data and problems bring to data-driven methodologies. Each ESR had to find a particular solution to address this and the individual research plan of each ESR has had to adapt to these developments.
The fellows’ contributions have demonstrated the variety of ways in which careful consideration of domain knowledge can help in devising new methods, representations, and models in the field of MIR, for a wide range of tasks. Knowledge-related questions are as important in MIR as they were years ago, when MIP-Frontiers proposal was written.
Clear advances have also been made in user-driven research. A fundamental requirement for modern Artificial Intelligence is that it is built for and with users, and MIP-Frontiers has also highlighted the necessity to further involve users in all possible states of algorithm development (design, interaction, evaluation,). This aspect is particularly important in this field since music is originally created and performed for humans.
All publications are open access and available in repositories and on the project website. The project has also included the publication of 15 open datasets.
At an industry level, the outcomes of one of the projects have already been licensed and will be available from DoReMIR in a new product from May 2022; in addition, Another ESR's work will be incorporated in a new product from Sony CSL. And the collaboration with Jamendo led to a new dataset that was used at 3 workshops (MediaEval).
