Blind Source Separation with Optimal Transport Non-negative Matrix Factorization

Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF) algorithm for supervised speech blind source separation (BSS). Optimal transport allows us to design and leverage a cost between short-time Fourier transform (STFT) spectrogram frequencies, which takes into account how humans perceive sound. We give empirical evidence that using our proposed optimal transport NMF leads to perceptually better results than Euclidean NMF, for both isolated voice reconstruction and BSS tasks. Finally, we demonstrate how to use optimal transport for cross domain sound processing tasks, where frequencies represented in the input spectrograms may be different from one spectrogram to another.Comment: 22 pages, 7 figures, 2 additional file

Menformulasi Pendidikan Pesantren Yang Membebaskan

Islam is a religion of liberation due to the fact that Islam appreciates human to be in equal level, maintains humanity, and higly supports the values democration and justice, teaches telling the truth, and loves the poor and the oppressed. Thus, Islamic education ideally could be conducted on the basis of equality and human dignity. This is to get rid of external hegemony an dit is to give priority to rightness, honesty and fairness, sincerity, and simplicity. The educational foundation might be used by the peasantren (Islamic boarding school) for the instrcutional practice. It is derived from the values that are developed through the educational process---independency, simplicity, and sincerity. The values become the armor for pesantren to get free from any kinds of life anomaly as the impact of the hegemony of modern life style and paradigm. This view concerns with materialism, hednism, opportunism, individualism, and consumerism.Kata-kata kunciPendidikan, pesantren, pembebasa

API design for machine learning software: experiences from the scikit-learn project

Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library