Publication date: Available online 1 November 2018
Source: Journal of Allergy and Clinical Immunology
Author(s): Victoria Garib, Eva Rigler, Felix Gastager, Raffaela Campana, Yulia Dorofeeva, Pia Gattinger, Yury Zhernov, Musa Khaitov, Rudolf Valenta
Publication date: Available online 1 November 2018
Source: Journal of Allergy and Clinical Immunology
Author(s): Ana Olivera, Dean D. Metcalfe
Publication date: Available online 1 November 2018
Source: Journal of Allergy and Clinical Immunology
Author(s): Theoharis C. Theoharides, Susan E. Leeman
Publication date: Available online 1 November 2018
Source: Journal of Voice
Author(s): Shaheen N. Awan, Jordan A. Awan
Objectives. The objective of this study was to investigate the ability of a two-stage method of cepstral peak identification to effectively discriminate rough vs breathy vs typical voice in sustained vowel productions. It was hypothesized that a dual-stage search for cepstral peak prominences (CPP's) above and below specified quefrency/F0 cutoffs would result in a CPP difference that would be characteristic of the rough, diplophonic voice type.
Methodology. Central one-second portions of sustained vowel /a/ productions were obtained from 90 subjects (rough, breathy, and normophonic voices). All voice samples were analyzed using a a two-stage cepstral analysis process in which a CPPHigh−Low difference value was obtained by identifying cepstral peaks above and below a lower limit for expected F0 (150 Hz for females and 90 Hz for males), called CPPHigh and CPPLow respectively.
Results. The CPPHigh−Low difference value was observed to be a highly significant predictor, with negative values for this parameter characteristic of a dominant subharmonic in the voice signal and the perception of diplophonic, rough voice. Correct classification of rough vs nonrough voice samples was 82.2% (sensitivity 0.80 and specificity 0.833). In the consideration of three group classification (breathy vs. normophonic vs. rough), models incorporating two predictors (the CPP obtained from a single search through a 60 to 300 Hz frequency range (CPPDefault) and the CPPHigh−Low difference value) correctly classified 78.88% of the voice samples.
Conclusions. Rough, diplophonic voices were consistently observed to have a subharmonic peak that was greater in amplitude than the cepstral peak obtained within the region of the expected F0, resulting in a negative value for the CPPHigh−Low difference. The two-stage cepstral analysis process described herein is visually intuitive from the graphical display of a cepstrum and is a simple extended calculation derived from cepstral analysis procedures that have been recommended as essential in the acoustic description of vocal quality.
Publication date: Available online 1 November 2018
Source: Medical Image Analysis
Author(s): Longwei Fang, Lichi Zhang, Dong Nie, Xiaohuan Cao, Islem Rekik, Seong-Whan Lee, Huiguang He, Dinggang Shen
Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a multi-atlas guided fully convolutional network (MA-FCN) for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of not only a training image patch but also a set of its neighboring (i.e., most similar) affine-aligned atlas patches. The guidance information from neighboring atlas patches can help boost the discriminative ability of the learned FCN. Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods.
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International Journal of Environmental Research and Public Health IJERPH, Vol. 17, Pages 6976: Overcoming Barriers to Agriculture Green T...
