Block Nonnegative Matrix Factorization for Single Channel Source Separation
نویسنده
چکیده
Nonnegative Matrix Factorization (NMF) [1, 2] has been widely used in audio research, e.g. automatic music transcription [3], musical source separation [4], and speech enhancement [5]. The key strategy for applying NMF to audio-related tasks is to find a lower rank representation of the Short Time Fourier Transformed (STFT) input signal and use the basis vectors as dictionaries. For example, in the single channel source separation, we assume that the dictionaries learned from different training sets of target sources are distinct so that their activations for reconstructing a mixture signal will have discriminant patterns per a source.
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