Modelling of Sound Events with Hidden Imbalances Based on Clustering and Separate Sub-Dictionary Learning

This paper proposes an effective modelling of sound event spectra with a hidden data-size-imbalance, for improved Acoustic Event Detection (AED). The proposed method models each event as an aggregated representation of a few latent factors, while conventional approaches try to find acoustic elements directly from the event spectra. In the method, all the latent factors across all events are assigned comparable importance and complexity to overcome the hidden imbalance of data-sizes in event spectra. To extract latent factors in each event, the proposed method employs clustering and performs non-negative matrix factorization to each latent factor, and learns its acoustic elements as a sub-dictionary. Separate sub-dictionary learning effectively models the acoustic elements with limited data-sizes and avoids over-fitting due to hidden imbalances in training data. For the task of polyphonic sound event detection from DCASE 2013 challenge, an AED based on the proposed modelling achieves a detection F-measure of 46.5%, a significant improvement of more than 19% as compared to the existing state-of-the-art methods

Psychiatric nurses’ observation techniques

The purpose of this study was to clarify characteristics of psychiatric nurses’ observation techniques for psychopathological symptoms. The subjects were 21 psychiatric nurses and 20 nursing students who had finished their practicum in psychiatric nursing. Using a non-contact eye-tracking analysis system, we compared quantitatively their radial motion while they were observing psychopathological symptoms of a schizophrenia simulation patient. The radial motion of them was recorded while they were observing a video of a simulated patient presenting psychopathological symptoms, and the recording was analyzed by the eye-tracking system. The investigator set the important observation areas and determined the sum of the fixation time and the number of fixations in the areas. Differences between psychiatric nurses and nursing students were tested using the Mann-Whitney U-test. The results revealed a significant difference in observation of the upper limbs area with a median of 7147.90 msec for nurses group and a median of 2447.54 msec for students group (U = 98.00, p = 0.01). The finding suggests that nurses tend to pay more attention to patient’s upper limbs to be cautious about possible violence and to find agitation caused by psychopathological symptoms, extrapyramidal adverse effects, and scars caused by self-mutilation