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By Yager R.R.

We examine the problem of database mining.An process is advised established uppon using fuzzy lingusitic summaries.

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It can locate the frequency bases of each source. On the whole, the NMPCF-based singing voice separation system presents good performance. -S. Huang et al. ): ICIC 2016, Part III, LNAI 9773, pp. 33–43, 2016. 1007/978-3-319-42297-8_4 34 Y. Hu et al. it scores lower too. g. when the singing-to-accompaniment ratio (SAR) is -9 dB, results are even slightly lower than that of a comparative separation system. In the field of single-channel signal separation, the researchers found that constraining the sparsity of basis vector belonging to a certain source is helpful for source separation [6, 7].

1s existed in Fig. 2(c). By the constraining of temporal continuity and sparsity criteria, those frequency components of non-singing voice are removed mostly, this can be seen in Fig. 2(d). Monaural Singing Voice Separation by NMPCF 39 Fig. 2. Spectrogram Comparison. (a) The Mixture spectrogram of accompanied vocal signal mixed at the SAR of −6 dB. (b) The Spectrogram of clean singing voice. (c) The spectrogram of the singing voice separated by “NMPCF”. (d) The spectrogram of the singing voice separated by “NMPCF + S+TC”.

Those monophonic mixture songs were first divided into vocal and non-vocal segments according the pitches of singing voice, and then those vocal segments were further segmented that each vocal segment lie within the range[1s, 4s]. There are 1568 vocal segments in all. The experiments were conducted over the segment data. In the experiments, a frame length of 32 ms and hop length of 16 ms with the sampling frequency of 16 kHz were used. The length of DFT was 512. The numbers of basis vectors were set to 100 for US and 50 for UA.

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