In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module can be plugged into any metric-based learning method for few-shot learning, allowing the resulting model to especially match related short sound events. Extensive experiments on two datasets show that the proposed module consistently improves the performance of five different metricbased learning methods for few-shot sound recognition. The relative improvement ranges from +4.1% to +7.7% for 5-shot 5-way accuracy for the ESC-50 dataset, and from +2.1% to +6.5% for noiseESC-50. Qualitative results demonstrate that our method contributes in particular to the recognition of transient sound events.

Making sense of the surrounding context and ongoing events through not only the visual inputs but also acoustic cues is critical for various AI applications. This paper presents an attempt to learn a neural network model that recognizes more than 500 different sound events from the audio part of user generated videos (UGV). Aside from the large number of categories and the diverse recording conditions found in UGV, the task is challenging because a sound event may occur only for a short period of time in a video clip. Our model specifically tackles this issue by combining a main subnet that aggregates information from the entire clip to make clip-level predictions, and a supplementary subnet that examines each short segment of the clip for segment-level predictions. As the labeled data available for model training are typically on the clip level, the latter subnet learns to pay attention to segments selectively to facilitate attentional segment-level supervision. We call our model the M&mnet, for it leverages both “M”acro (clip-level) supervision and “m”icro (segment-level) supervision derived from the macro one. Our experiments show that M&mnet works remarkably well for recognizing sound events, establishing a new state-of-theart for DCASE17 and AudioSet data sets. Qualitative analysis suggests that our model exhibits strong gains for short events. In addition, we show that the micro subnet is computationally light and we can use multiple micro subnets to better exploit information in different temporal scales.

Easily accessible biomarkers for Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.

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