High-Resolution Representation Learning and Recurrent Neural Network for Singing Voice Separation

Abstract

Music source separation has traditionally followed the encoder-decoder paradigm (e.g., hourglass, U-Net, DeconvNet, SegNet) to isolate individual music components from mixtures. Such networks, however, result in a loss of location-sensitivity, as low-resolution representation drops the useful harmonic patterns over the temporal dimension. We overcame this problem by performing singing voice separation using a high-resolution representation learning (HRNet) system coupled with a long short-term memory (LSTM) module to retain high-resolution feature map and capture the temporal behavior of the acoustic signal. We called this joint combination of HRNet and LSTM as HR-LSTM. The predicted spectrograms produced by this system are close to ground truth and successfully separate music sources, achieving results superior to those realized by past methods. The proposed network was tested using four datasets (DSD100, MIR-1K, Korean Pansori, and Nepal Idol singing voice). Our experiments confirmed that the proposed HR-LSTM outperforms state-of-the-art networks at singing voice separation when the DSD100 dataset is used, performs comparably to alternative methods when the MIR-1K dataset is used, and separates the voice and accompaniment components well when the Pansori and NISVS datasets are used. In addition to proposing and validating our network, we also developed and shared our Nepal Idol dataset.

Symmetrical Impulsive Inertial Neural Networks with Unpredictable and Poisson-Stable Oscillations

“…Proof. The coordinate u(t) of system (7) is the desired Poisson-stable solution of the main system (4). Therefore, we first prove the existence of such a unique exponentially stable, discontinuous Poisson-stable solution of system (7).…”

“…The coordinate u(t) of system (7) is the desired Poisson-stable solution of the main system (4). Therefore, we first prove the existence of such a unique exponentially stable, discontinuous Poisson-stable solution of system (7). We start with the proof of the completeness of the space Ξ. Denote a Cauchy sequence by ω l (t) = {ω l i (t)}, i = 1, 2, .…”

“…. m in system (7) belong to Ξ, and they are unpredictable with positive numbers 0 , δ and divergence sequence s n , n ∈ N.…”

“…. , v m (t)) is a bounded solution of (7) if and only if it is a solution of the following integral equation:…”

“…The technology of neural computing networks has shown its effectiveness in solving problems of pattern recognition, data clustering, associative information retrieval in databases, and in a number of other applications [1][2][3][4][5][6][7].…”