107 resultados para Nonlinear acoustics


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The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

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Cervical auscultation (CA) may be used to complement the clinical feeding examination when assessing for oropharyngeal aspiration (OPA). Data exists on the acoustic properties of normal and abnormal swallowing sounds in adults and children. However, there are no published paediatric studies comparing the acoustic properties of sounds comparing OPA with non-OPA swallows. We aimed to determine if there is an acoustic difference between modified barium swallow (MBS)-identified OPA and non-OPA swallow sounds in children.