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====== Example Project Title ====== | Authors | Steve Jobs and Bill Grates | | Affiliation | Apple and Michaelsoft | | Code | [[https://github.com/bmcfee/librosa|Github Link]] | Singing voice separation task is to separate singing voices from music accompaniment. We propose spoken lyrics informed source separation. ===== - Background: Singing Voice Separation ===== * ROBUST PRINCIPAL COMPONENT ANALYSIS\\ minimize $||A||_*+ \lambda ||E||_1$, subject to $A+E = M$\\ Music accompaniment can be assumed to be in a low-rank subspace, because of its repetition structure.\\ Singing voices can be regarded as relatively sparse within songs. * Nonnegative matrix factorization ===== - Proposed Spoken Lyrics-informed source separation ===== * HAMR-RPCA * HAMR-NMF ==== - HAMR-RPCA ==== * HAMR-RPCA\\ minimize $||A||_* + \lambda ||E||_1 + \gamma || E - E_0 ||_F ^2$ subject to $A+E = M$\\ * Frame work\\ Given mixed signals, run RPCA to obtain $E_{RPCA}$.\\ We use dynamic time warping to warp spoken lyrics E_{spoken} to E_{RPCA}.\\ Define E_0 as the E_{spoken} for HAMR-RPCA. (Results -- with ground truth singing voice as E_0) (Results -- with ground truth singing voice from spoken lyrics) Conclusion ==== - Title of a subsection ==== Etc. etc. Some math: $\beta = \sum_{i = 1}^N \sqrt{\alpha_i^2 - G^2}$ Separation with codebook Another possible extension involves the introduction of a pre-learnt dictionary in the separation process. As we know, the supervised learning algorithms can usually lead to better result in the machine learning problems. Following the intuition mentioned above, to incorporate the information from the spoken lyrics, we proposed a dictionary based model as the extension of RPCA framework. The basic steps are as follows: - Ordered List Item 1 - 2 - 3 - 4 sad
