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This paper makes the first attempt to utilize convolutional neural network (CNN) for classification of solar radio spectrums. The solar radio spectrum is a two-dimensional gray-scale image with one dimension of frequency and the other of time. Taking the advantages of CNN, we can efficiently learn the distinct characteristic of different types of spectrum, and further classify them even more accurate...
Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks. However, little is known about its effectiveness in other challenging situations such as music source separation. Contrary to conventional networks that directly estimate the...
A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetic discriminate/speaker discriminate DNN as a feature extractor for speaker verification has shown promising results. The extracted frame-level (bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation...
We address the problem of "cocktail-party" source separation in a deep learning framework called deep clustering. Previous deep network approaches to separation have shown promising performance in scenarios with a fixed number of sources, each belonging to a distinct signal class, such as speech and noise. However, for arbitrary source classes and number, "class-based" methods...
In this paper, the authors make the first attempt to employ the deep learning method for the representation learning of the solar radio spectrums. The original solar radio spectrums are pre-processed, including normalization, enhancement and etc., to generate new images for the next processing. With the expertise of solar radio astronomy for identifying solar radio activity, we build a solar radio...
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