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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,786 papers · 148 categories

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6 results for “remixing”

Demucs: Deep Extractor for Music Sources with extra unlabeled data remixedcs.SD

Improved music source separation using unlabeled data remixing.

problem Music source separation with deep learning.

method Introduces a simple convolutional and recurrent model and a scheme to leverage unlabeled music.

result Waveform methods can now match spectrogram methods on standard benchmarks.

music source separation deep learning waveform models unlabeled data remixing

2019-09-03abs ↗pdf ↗

Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remixeess.SP

Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression …

2018-12-14abs ↗pdf ↗

Voice and accompaniment separation in music using self-attention convolutional neural networkeess.AS

A new neural network separates vocals from music accompaniment.

problem Separating vocals from music accompaniment in recordings.

method Self-attention convolutional neural network (CNN) with densely-connected blocks.

result 19.5% relative improvement in vocals separation.

music separation voice separation self-attention convolutional neural network music accompaniment

2020-03-19abs ↗pdf ↗

The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatchcs.CV

SVHN dataset's split affects generative models but not digit classification.

problem Distribution mismatch between SVHN training and test sets impacts generative models.

method Empirically showed distribution mismatch affects generative models; proposed mixing and re-splitting.

result Distribution mismatch in SVHN dataset significantly impacts probabilistic generative models.

SVHN dataset distribution mismatch generative models Variational Autoencoders diffusion models

2023-10-30abs ↗pdf ↗

AR-Flow VAE: A Structured Autoregressive Flow Prior Variational Autoencoder for Unsupervised Blind Source Separationstat.ML

AR-Flow VAE improves blind source separation with flexible autoregressive priors.

problem Unsupervised blind source separation of latent signals from mixtures.

method AR-Flow VAE uses autoregressive flows to model latent sources, enhancing flexibility and capturing complex dependencies.

result AR-Flow VAE effectively separates latent sources, demonstrating improved performance over conventional methods.

blind source separation autoregressive flow variational autoencoder flexible priors non-Gaussian modeling

2026-03-15abs ↗pdf ↗

Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detectioneess.AS

Paper proposes MTL for weakly labelled SED, improving performance with 2-step attention.

problem Weakly labelled sound event detection.

method Multi-Task Learning framework with 2-step Attention Pooling.

result Improved SED performance with 22.3%, 12.8%, 5.9% gains at 0, 10, 20 dB SNR.

Multi-Task Learning Weakly Labelled Sound Event Detection Attention Pooling TimeFrequency Representation

2020-08-17abs ↗pdf ↗