This paper proposes a novel approach to accomplish the automatic segmentation of singing voice within music signals, based on the difference between the dynamic harmonic content of singing voice and that of musical instrument signals. The obtained results are compared with those of another approach proposed in the literature, considering the same music database. For both techniques, an accuracy rate around 80% is obtained, even using a more rigorous performance measure for our approach only. As an advantage, the new procedure presents lower computational complexity. In addition, we discuss other results obtained by extending the tests over the whole database (upholding the same performance level) and by discriminating the error types (boundaries shifted in time, insertion and deletion of
singing segments). The analysis of these errors suggests some alternative ways of reducing them, as for example, to adopt a confidence level based on a minimum harmonic content for the input signals. In this way, considering only signals with confidence level equal to one, the obtained performance is improved to almost 87%.
We present a new technique for extracting the direction map from fingerprints. The fingerprint image is first partitioned into small image blocks. Then, a set of parameters is extracted from each block and fed into a neural network that outputs the preferential direction for each block. The technique performed very well in operational conditions. It was developed to be employed in an Automatic Fingerprint Classification System.
This paper presents an interesting algorithm, suitable for substantial noise reduction in images with 2(pi) phase jumps. It first applies a cosine transform to the phase map associated with the corresponding fringe pattern then filters (low pass) the resulting images and computes phase again from both images. Since the cosine transform is invariant to a 2(pi) phase jumps the low pass filtering does not distort the measuring signal in such regions.
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