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The human auditory system is very well matched to both human speech and environmental sounds. Therefore, the question arises whether human speech material may provide useful information for training systems for analyzing nonspeech audio signals, such as in a recognition task. To find out how similar nonspeech signals are to speech, we measure the closeness(More)
Despite the success of the automatic speech recognition framework in its own application field, its adaptation to the problem of acoustic event detection has resulted in limited success. In this paper, instead of treating the problem similar to the segmentation and classification tasks in speech recognition, we pose it as a regression task and propose an(More)
This paper proposes an approach for the efficient automatic joint detection and localization of single-channel acoustic events using random forest regression. The audio signals are decomposed into multiple densely overlapping superframes annotated with event class labels and their displacements to the temporal starting and ending points of the events. Using(More)
We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multiple convolutional and pooling layers topped up with multiple fully connected layers, the proposed network consists of only three layers: convolutional, pooling, and softmax layer.(More)
Pneumolysin is an important virulence factor of Streptococcus pneumoniae. This study examined the hypothesis that human antibody to pneumolysin provides protection against pneumococcal infection. At the time of hospital admission, patients with nonbacteremic pneumococcal pneumonia had higher levels of serum anti-pneumolysin IgG than did patients with(More)
Studies of myocardial metabolism have reported that contractile performance at a given myocardial oxygen consumption (MVO2) can be lower when the heart is oxidizing fatty acids rather than glucose or lactate. The objective of this study is to assess the prognostic value of myocardial metabolic phenotypes in identifying non-responders among non-ischemic(More)
We introduce in this paper a concept of using acoustic superframes, a mid-level representation which can overcome the drawbacks of both global and simple frame-level representations for acoustic events. Through superframe-level recognition, we explore the phenomenon of superframe co-occurrence across different event categories and propose an efficient(More)
Detection of immunoglobulin G (IgG) antibody to pneumolysin (PLY) in precipitated circulating immune complexes (CICs) has been used to diagnose pneumococcal pneumonia. With care to include appropriate controls, we precipitated and dissociated CICs and then assayed for IgG antibody to PLY. We detected IgG antibody to PLY in CICs that were precipitated from(More)
Audio event detection has been an active field of research in recent years. However, most of the proposed methods, if not all, analyze and detect complete events and little attention has been paid for early detection. In this paper, we present a system which enables early audio event detection in continuous audio recordings in which an event can be reliably(More)
Recognizing acoustic events is an intricate problem for a machine and an emerging field of research. Deep neural networks achieve convincing results and are currently the state-of-the-art approach for many tasks. One advantage is their implicit feature learning, opposite to an explicit feature extraction of the input signal. In this work, we analyzed(More)