No abstract

No abstract

The unoccupied electronic structure of stacked layers of copper(II)phthalocyanine (CuPc) and perylene-3,4,9,10-tetracarboxylic dianhydride (PTCDA) on Ag(1 1 1) has been investigated by means of two-photon photoemission (2PPE). We find a rich electronic structure comprising at least five unoccupied electronic states which we identify based on their energetic position and their dispersion in momentum space. More specifically, we observe the first and the second image-potential states of the modified Ag(1 1 1) surface, as well as the metal-organic interface state (IS) inherent to the PTCDA/Ag(1 1 1) interface. Moreover, two additional molecular features are observed for the CuPc/PTCDA/Ag(1 1 1) system which we attribute to an unoccupied molecular orbital (LUMO + 2) of CuPc. The 2PPE intensity of the IS exhibits a pronounced dependence on the pump photon energy, which closely follows the optical absorption of the outer molecular layer. This strongly points to charge transfer from the optically excited molecules to the interface state.

No abstract

In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often defining the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classification of drum sound events by computational methods is considered to be an important and challenging research problem in the broader field of Music Information Retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term Automatic Drum Transcription (ADT). This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on Nonnegative Matrix Factorization and Recurrent Neural Networks. We explain the methods' technical details and drum-specific variations and evaluate these approaches on publicly available datasets with a consistent experimental setup. Finally, the open issues and under-explored areas in ADT research are identified and discussed, providing future directions in this field.