Paper introduces a new time separation function for C0 spacetimes.
problem Lower semicontinuity of time separation function for C0 spacetimes. method Introduced nearly timelike curves to ensure lower semicontinuity.
result Lower semicontinuous time separation function for C0 spacetimes. Paper shows how SFA fits into FBM framework for time series separation.
problem Identifying time series decomposition in flow-based models.
method Combining SFA and FBM to make time series decomposition identifiable.
result Time series decomposition becomes identifiable using SFA and FBM.
Paper separates financial time series into fast and slow components.
problem Multiscale behavior in financial time series data.
method Uses variance and tail stationarity criteria as generalized eigenvalue problems.
result Identifies slow and fast components in asset returns and prices.
Efficient sparse GP model improves audio source separation.
problem Sparse Gaussian Process (GP) inference is computationally expensive for long audio frames.
method Used GP regression, spectral mixture kernels, and variational sparse GPs.
result Proposed method outperforms LD-PSDTF, KL-NMF, and IS-NMF.
Improved speech separation and enhancement using neural beamforming.
problem Challenging speech separation and enhancement in reverberant environments.
method Sequential neural beamforming combining spectral and spatial separation methods.
result Average improvement of 2.75 dB in scale-invariant signal-to-noise ratio and 14.2% absolute reduction in speech recognition metric.
Separates Dirac equation on 2D product spaces and black hole horizons.
problem Separating Dirac equation on complex spacetime backgrounds.
method Used separation of variables in 2D product spaces to solve Dirac equation.
result Dirac equation separable in specific static black hole solutions.
Reservoir computing's success depends on mapping different input time series to separable states.
problem Quantifying the ability of random linear reservoirs to map different input time series.
method Mathematical framework using spectral properties of the connectivity matrix.
result Separation capacity is fully characterized by the spectral properties of the connectivity matrix.
Paper investigates separating times for general diffusions, providing new insights.
problem Understanding phase transitions between equivalence and singularity in diffusions.
method Representation of separating time as hitting time of a deterministic set, characterized by speed and scale.
result Explicit and easy-to-check conditions for absolute continuity and singularity of diffusions.
New algorithm separates vocals from music recordings efficiently.
problem Separate vocal and instrumental parts in music recordings.
method Informed group-sparse representation for linear-time singing voice separation.
result Efficacy confirmed on iKala dataset; music accompaniment follows group-sparse structure.
Deep learning models improve sound separation across various types of sounds.
problem Developing a universal method to separate arbitrary sounds of different types.
method Created a dataset of mixtures containing arbitrary sounds, investigated mask-based separation architectures, and tested different framewise analysis-synthesis bases.
result STFT outperformed learnable bases in universal sound separation tasks.
The paper establishes a nearly-sharp statistical threshold for efficient learning in Latent MDPs with separated components.
problem Learning Latent Markov Decision Processes (LMDPs) with separated components.
method The paper considers various notions of separation and establishes a nearly-sharp statistical threshold for efficient learning. It also presents a quasi-polynomial algorithm with time complexity scaling in terms of the statistical threshold under a weaker assumption of separability under the optimal policy, and a near-matching time complexity lower bound under the exponential time hypothesis.
result Establishes a nearly-sharp statistical threshold for efficient learning in Latent MDPs with separated components.
Wave-U-Net improves audio source separation by modeling phase information.
problem Fixed spectral transformations and high sampling rates limit audio source separation performance.
method Wave-U-Net adapts U-Net to time-domain, using repeated resampling to capture different time scales.
result Wave-U-Net achieves comparable performance to spectrogram-based U-Net on singing voice separation.
Polynomial-time algorithm for clustering mixtures with separation Δ=Ω(√(log k)).
problem Clustering mixtures of mean-separated Gaussians in high dimensions.
method Polynomial-time algorithm using implicit moment estimation.
result Achieves almost optimal clustering guarantee with separation Δ=Ω(√(log k)).
A new method reduces computational time for source separation and classification.
problem High computational complexity and unsatisfactory source classification accuracy in MVAE.
method Integrates an auxiliary classifier VAE to reduce computational time and improve classification accuracy.
result fMVAE achieved comparative source separation performance and 80% source classification accuracy while reducing computational time by 93%.
The study shows subgroup separability conditions for specific groups.
problem Conditions for subgroup separability in free-by-cyclic and deficiency 1 groups.
method Analyzes polynomially growing monodromy and asymptotic probability of random groups.
result Random deficiency 1 groups are not subgroup separable with positive probability.
Wave-U-Net with MHE regularization improves singing voice separation.
problem Singing voice separation from mixed music recordings.
method Wave-U-Net architecture with MHE regularization applied to 1D filters.
result Adding MHE regularization to the loss function consistently improves singing voice separation.
Improves speech separation by integrating time and frequency domains.
problem Speech separation using deep learning techniques.
method Proposes a framework that combines time and frequency domain features, using an embedding network and clustering.
result Obtained state-of-the-art results on WSJ0-2mix dataset.
This paper provides a mathematical framework for time-delay reservoir computing.
problem Lack of rigorous mathematical foundations for reservoir computing properties.
method Control-theoretic framework, formal definitions of separation and fading memory, explicit lower bound derivation.
result Established formal definitions and connections to stability notions for time-delay systems.
New non-separable covariance kernels for spatiotemporal data derived from harmonic oscillator physics.
problem Capturing complex spatiotemporal dependencies in Gaussian processes.
method Hybrid spectral method based on the harmonic oscillator, deriving explicit covariance kernels.
result Explicit non-separable covariance kernels with space-time interactions.
DMD separates mixed time series with uncorrelated components.
problem Separating mixed time series with uncorrelated components.
method Dynamic Mode Decomposition (DMD) applied to a data matrix of mixed time series.
result DMD can approximate the mixing matrix of uncorrelated time series.
Paper presents a provably correct algorithm for CNMF under separable conditions.
problem Convolutive nonnegative matrix factorization (CNMF) under separable assumptions.
method Algorithm exploiting NMF model and existing separable NMF algorithms.
result Guaranteed solution in low noise settings, runs in polynomial time.
Analyzes memory time span in LSTMs for multi-speaker speech separation.
problem Understanding how long-term dependencies are handled by LSTMs in speech separation tasks.
method Leaked state variable with controlled lifetime to evaluate task performance.
result Estimates the time span LSTMs exploit in multi-speaker speech separation.
Heuristic algorithm for portfolio optimization reduces solve times to milliseconds.
problem Mean-variance portfolio optimization with various constraints.
method Alternating Direction Method of Multipliers (ADMM).
result Achieves performance bounds and solves problems in milliseconds.
Adam optimizes linear classifiers with separable data.
problem Understanding Adam's implicit bias in linear logistic regression.
method Study of Adam's behavior on linearly separable data.
result Adam converges to a linear classifier with maximum ℓ∞-margin. A new method for separating mixed signals in space and time.
problem Nonlinear and nonstationary spatio-temporal data challenges.
method Identifiable autoregressive variational autoencoder.
result The method outperforms existing techniques in blind source separation and spatio-temporal prediction.
We consider Aubry-Mather theory for a subclass of class A spacetimes, i.e. compact vicious spacetimes with globally hyperbolic Abelian cover. In this subclass, called class A_1, we obtain improved results on timelike maximizers and Lipschitz continuity of the time separation of the Abelian cover on the i.g. optimal sub…
Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality i…
Proposes a two-step method for sound source separation.
problem Improving sound source separation performance.
method First, learn a latent space transform. Second, train a separation module in the latent space.
result The proposed method achieves better performance than joint learning approaches.
Proposes a new interpretation of separable convolutions.
problem Lack of a thorough explanation for the efficacy of separable convolutions.
method Hybrid interpretation combining depthwise and pointwise convolutions.
result Proposes a new model for understanding separable convolutions.
New method separates sounds with weak labels in noisy environments.
problem Training audio source separation systems with limited labeled data.
method Proposes objective functions and network architectures for weakly labeled training.
result Achieves significant SI-SDR improvement in noisy scenarios.
We show that all groups in a very large class of Coxeter groups are locally quasiconvex and have uniform membership problem solvable in quadratic time. If a group in the class satisfies a further hypothesis it is subgroup separable and relevant homomorphisms are also calculable in quadratic time. The algorithm also dec…
We introduce a new approach for designing computationally efficient learning algorithms that are tolerant to noise, and demonstrate its effectiveness by designing algorithms with improved noise tolerance guarantees for learning linear separators. We consider both the malicious noise model and the adversarial label nois…
A new method for blind source separation using hierarchical structure and KL divergence.
problem Blind source separation of complex interacting signals.
method Hierarchical log-linear model with KL divergence minimization.
result Superior performance compared to existing techniques on images and time series data.
New method separates audio sources without needing known decompositions.
problem Difficulty in training source separation models on real-world mixtures.
method Generates estimated decompositions from stereo mixtures and trains a deep learning model.
result Trained model can separate single-channel audio sources effectively.
A new NMF variant tackles underdetermined problems with sparse and separable assumptions.
problem Underdetermined blind source separation, especially multispectral image unmixing.
method Sparse Separable Nonnegative Matrix Factorization (SSNMF) combining separability and sparsity assumptions. Algorithm based on SNPA and sparse nonnegative least squares.
result In noiseless settings, the algorithm recovers true underlying sources.
We augment the nonnegative matrix factorization method for audio source separation with cues about directionality of sound propagation. This improves separation quality greatly and removes the need for training data, with only a twofold increase in run time. This is the first method which can exploit directional inform…
New algorithms reduce slate bandit regret for large slates, outperforming existing methods.
problem Non-separable reward functions in slate bandits with many slates.
method Design of algorithms with sub-linear regret.
result Sub-linear regret with respect to the time horizon for large number of slates.
Algorithm distinguishes Gaussian mixtures from pure Gaussians in quasi-polynomial time.
problem Distinguishing mixtures of Gaussian components from pure Gaussians, especially when components are well-separated.
method Sum-of-Squares method, quasi-polynomial time algorithm, bipartitioning sample to separate components.
result Algorithm can reliably distinguish between mixtures and pure Gaussians in quasi-polynomial time.
DR-NMF uses unfolded ISTA for speech separation, offering interpretability and speed.
problem Speech separation in noisy environments.
method DR-NMF is a recurrent neural network that unfolds ISTA iterations for NMF of spectrograms.
result DR-NMF outperforms NMF and LSTM networks in speech separation.
A new neural network separates singing voices more effectively.
problem Separating singing voices from mixed signals with high accuracy.
method MBR-FCN that processes different frequency bands with varying resolutions and filters.
result The MBR-FCN achieves better performance with fewer parameters.
Deep clustering outperforms conventional networks in singing voice separation.
problem Challenging music source separation tasks.
method Combining deep clustering and conventional networks for better performance.
result Hybrid network outperforms both components.
Q(Δ)-Learning improves Q-Learning by separating action-value functions into different time scales.
problem Q-Learning struggles with bias-variance trade-off, especially in long-term rewards.
method Introduces Q(Δ)-Learning, extending TD(Δ) to decompose Q(Δ)-function into distinct discount factors. result Q(Δ)-Learning achieves better stability and scalability, especially for long-term tasks. VoiceFilter-Lite separates speech from background in real-time for on-device speech recognition.
problem Separate speech from background in real-time for on-device speech recognition.
method Asymmetric loss, adaptive runtime suppression, quantization to 8-bit.
result VoiceFilter-Lite achieves real-time speech separation and maintains speech recognition performance.
A new method separates instruments in music using shared embeddings.
problem Separating individual instruments in a musical mixture.
method Common embedding space for all instruments, auxiliary Gaussian mixture model.
result Outperforms mask-inference baseline on MUSDB-18 dataset.
A new unsupervised method separates speech sources without requiring labeled data.
problem Lack of supervised data for effective neural source separation.
method Uses a complex Gaussian mixture model (cGMM) for joint training of separation and localization networks.
result The method outperforms conventional initialization methods in monaural and multichannel separation.
Mirror flow optimizes separable data problems, converging to a maximum margin classifier.
problem Optimizing classification problems with separable data using mirror flow.
method Examine mirror flow on linearly separable classification problems, focusing on the horizon function of the mirror potential.
result Mirror flow converges to a maximum margin classifier for separable data under certain conditions.
We propose a principle and loss functions for efficient single-class classification.
problem Efficient binary classification for specific classes in high-dimensional data.
method Define Single Logit Classification (SLC) task, propose Principle of Logit Separation, and design loss functions.
result Loss functions aligned with the Principle of Logit Separation improve SLC accuracy by 20%.
New algorithm separates audio sources better using alpha-stable distributions.
problem Improving audio source separation using complex distributions.
method Estimating mixtures of alpha-stable distributions using characteristic function matching.
result Better separation performance than Gaussian-based methods.