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Introduction: Humans tend to categorize auditory stimuli into discrete classes, such as animal species, language, musical instrument, and music genre. Of these, music genre is a frequently used dimension of human music preference and is determined based on the categorization of complex auditory stimuli. Neuroimaging studies have reported that the superior temporal gyrus (STG) is involved in response to general music‐related features. However, there is considerable uncertainty over how discrete music categories are represented in the brain and which acoustic features are more suited for explaining such representations. Methods: We used a total of 540 music clips to examine comprehensive cortical representations and the functional organization of music genre categories. For this purpose, we applied a voxel‐wise modeling approach to music‐evoked brain activity measured using functional magnetic resonance imaging. In addition, we introduced a novel technique for feature‐brain similarity analysis and assessed how discrete music categories are represented based on the cortical response pattern to acoustic features. Results: Our findings indicated distinct cortical organizations for different music genres in the bilateral STG, and they revealed representational relationships between different music genres. On comparing different acoustic feature models, we found that these representations of music genres could be explained largely by a biologically plausible spectro‐temporal modulation‐transfer function model. Conclusion: Our findings have elucidated the quantitative representation of music genres in the human cortex, indicating the possibility of modeling this categorization of complex auditory stimuli based on brain activity. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have been regarded as maintenance-free; however, numerous failures have been reported recently during their operation. These failures not only cause power drops in the PV system but can also lead to serious accidents. This paper proposes a diagnosis method that utilizes the current-voltage (I-V) measurements of the PV string to detect the existence of a power drop module in the PV system. This diagnostic methodology is based on the calculation of the decrease in the area of the string I-V curve and dP/dV-V characteristic from the obtained data. A power drop module in the PV string is assumed and calculated. First, the normal and failed versions of the PV string are distinguished by plotting the decrease in the area of the I-V curve on one axis and the ratio of the power drop on the other axis. The differences among each failure mode are then obtained by evaluating the dP/dV-V characteristic of the PV string. Finally, experimental tests are performed on the PV string to validate the proposed method of detecting power drop. [ABSTRACT FROM AUTHOR]

This dataset includes functional magnetic resonance imaging (fMRI) data collected while five subjects extensively listened to 540 music pieces from 10 music genres over the course of 3 days. Behavioral data are also available. Data are separated into training and test samples to facilitate the application of machine learning algorithms. Test stimuli were repeated four times and can be used to evaluate the signal to noise ratio of brain activity. Using this dataset, both neuroimaging and machine learning researchers can test multiple algorithms regarding the prediction performance of brain activity induced by various music stimuli. Although two previous studies have used this dataset, there remains much room to apply different acoustic models. This dataset can contribute to integration of the fields of auditory neuroscience and machine learning., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Published by Elsevier Inc.)