Challenges and Opportunities. Are there new challenges or opportunities that you experienced this year that may require significant attention, resources, or organizational effort in the coming year? San Diego CPPS Fund Evaluation:
Challenges and Opportunities. Despite the constantly increasing availability of instrument recognition and source separation datasets, they are usually designed for just one of those tasks. So, they lack the necessary annotations for performing the other task. Therefore, we need to create our own dataset to tackle these two tasks together and then apply them to improving automatic transcription. Moreover, novel deep-learning architectures and user-driven methods will also be developed as result of the project. One example is a proposal of a novel architecture for performing harmonic-percussive source separation [13] that was already accepted for publication.
Challenges and Opportunities. The dataset we recorded [4] will potentially bring robustness to the algorithms for drum sample query by vocal percussion. The VocalSketch dataset [5] seems ideal to study when it comes to the rest of the query sounds and synth parameter configurations, and therefore there is no need to record a new dataset for those.
Challenges and Opportunities. Some commercial and community databases of loops for electronic music production offer audio loops which are annotated with the predominant instrument in the loop. By collecting a large enough dataset from these databases, we can use deep learning approaches for automatically identifying the predominant instrument. Techniques for data augmentation using digital audio effects will be employed to improve the robustness and the classification accuracy of a deep learning model for instrument classification.
Challenges and Opportunities. Previous work has not studied the relationship between audio content and contextual information. One challenge is to identify to what degree the context of the user affects the choice of music style. Is there a dominant acoustic feature that prevails in each of these contexts? Are some of these contexts more influential in choosing specific music style than others? All of these questions require a joint study of audio content and user’s context. This would be helpful in identifying the importance of certain contexts in the recommendation process and help in auto-tag tracks with their suitable context classes.
Challenges and Opportunities. Music data nowadays is available in very large quantities and the number and type of annotations are constantly increasing. However, this is not true when considering as annotation, the physiological response of the user who is listening to the music. Such annotations are expensive and time-consuming to obtain. This is the main reason why only a few and small datasets are available in this field. We acquired our own dataset, but the amount of data collected is still not enough to develop reliable data-driven techniques to characterize the user’s attention with respect to music.
Challenges and Opportunities. From the current state of the art as described above, and from our goal of extending this to a massive scale, a number of specific challenges and opportunities for original research follow. Our motivation is to develop robust, scalable methods for supporting several related tasks: retrieval of one modality based on another (e.g. retrieval of audio recordings given score image queries); alignment of multiple performances to sheet music for purposes of score-based listening and comparison; and piece identification in unknown recordings, e.g., for automatic metadata provision. Since our goal is to extend state of the art methods to a massive scale, one crucial aspect of our research will be to identify, and possibly augment, potential data to be exploited. First, the MSMD dataset mentioned above is still a suitable starting point for our purposes. Although completely artificial, it could be re- rendered for different instrumentation and/or genres. Moreover, various creative and musically meaningful forms of data augmentation (which has proven to be an extremely effective method in many applications of deep learning) will have to be investigated. Of course, also ways of extending the number and diversity of musical pieces (and real interpretations of these) will be targeted. With respect to the use of real (real-world) score images, the IMSLP Xxxxxxxx Music Library (which contains over 400,000 scores and 50,000 recordings) is a promising online data source that will be investigated for this project.
Challenges and Opportunities. Radical reforms face numerous barriers and challenges. Proposed reforms have met heavy resistance from medical personnel at all levels. Despite MOH communication efforts, many physicians still complain about not being informed and/or consulted about the directions of reforms. Many of them fear losing jobs and informal income. In many cases, chief doctors of hospitals and heads of Oblast Healthcare Departments are conservative and resistant to
Challenges and Opportunities. USAID/Nepal has identified several factors that exemplify the challenges and opportunities toward improving disaster risk management in Nepal.
Challenges and Opportunities. There are a few items to consider for the commercial application of a 10 MW wind turbine: • The reference turbine is a scaled up conventional design. This may not lead to the optimum economic design. Design optimization at this scale is desired. • The industrialization of large main components and the availability of large offshore bespoke installation and maintenance vessels for this scale of turbines is progressing in parallel with turbine development. Although this results in a development risk, providing that turbine design work is being done in close communication with potential installation contractors, the risk can be minimised. The following opportunities can mitigate risks and reduce time to market: • Better insights in the cost breakdown. • Availability of onshore test locations for prototype testing. Ideally these test locations should be near the coast with high annual wind speed. The noise emission constraints at the site should not be limiting the turbine tip speed during testing.
