Robust music signal separation based on supervised nonnegative matrix factorization with prevention of basis sharing

Kitamura, Daichi; Saruwatari, Hiroshi; Yagi, Kosuke; Shikano, Kiyohiro; Takahashi, Yu; Kondo, Kazunobu

doi:10.1109/isspit.2013.6781913

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Bisot et al [43] applied supervised NMF to acoustic scene classification and obtained rather good performance. Sprechmann et al [44] and Weninger et al [81] solved the audio source separation with supervised NMF, while Nakajima et al [82] and Kitamura et al [83] adopted supervised NMF for music signal separation.…”

Section: Speech Recognitionmentioning

confidence: 99%

Recent Advances in Supervised Dimension Reduction: A Survey

Chao

Luo

Ding

2019

MAKE

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Recently, we have witnessed an explosive growth in both the quantity and dimension of data generated, which aggravates the high dimensionality challenge in tasks such as predictive modeling and decision support. Up to now, a large amount of unsupervised dimension reduction methods have been proposed and studied. However, there is no specific review focusing on the supervised dimension reduction problem. Most studies performed classification or regression after unsupervised dimension reduction methods. However, we recognize the following advantages if learning the low-dimensional representation and the classification/regression model simultaneously: high accuracy and effective representation. Considering classification or regression as being the main goal of dimension reduction, the purpose of this paper is to summarize and organize the current developments in the field into three main classes: PCA-based, Non-negative Matrix Factorization (NMF)-based, and manifold-based supervised dimension reduction methods, as well as provide elaborated discussions on their advantages and disadvantages. Moreover, we outline a dozen open problems that can be further explored to advance the development of this topic.

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Section: Speech Recognitionmentioning

confidence: 99%

Recent Advances in Supervised Dimension Reduction: A Survey

Chao

Luo

Ding

2019

MAKE

View full text Add to dashboard Cite

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Machine learning on neutron and x-ray scattering and spectroscopies

Chen

Andrejevic

Drucker

et al. 2021

Chemical Physics Reviews

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Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision.These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more Page 2 of complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.

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