4 accepted papers
In recent years, prototypical networks have been widely used in many few-shot learning scenarios. However, as a metric-based learning method, their performance often degrades in the presence of bad or noisy embedded features, and outliers in support instances. In this paper, we introduce a hybrid at…
Recently, many attention-based deep neural networks have emerged and achieved state-of-the-art performance in environmental sound classification. The essence of attention mechanism is assigning contribution weights on different parts of features, namely channels, spectral or spatial contents, and te…
Several architectures have been proposed for deep neural network (DNN)-based speech enhancement; however, these all utilize training targets related to the clean speech signal. In this paper, we evaluate the performance of several training targets in a noise-prediction DNN framework and compare the…
Researchers have recently examined a modified approach to sparse coding that encourages dictionaries to learn anomalous features. This is done by incorporating the matrix I-norm, or l1,∞ mixed matrix norm, into the dictionary update portion of a sparse coding algorithm. However, solving a matrix nor…