Papers Audio Source Separation
“Audio Source Separation” 태그가 달린 논문 117편 · 필터 해제
Differentiable Model Compression via Pseudo Quantization Noise
We propose DiffQ a differentiable method for model compression for quantizing model parameters without gradient approximations (e.g., Straight Through Estimator). We suggest adding independent pseudo quantization noise t…
Audio Source Separationimage-classificationImage ClassificationLanguage Modeling+4Weakly-supervised Audio-visual Sound Source Detection and Separation
Learning how to localize and separate individual object sounds in the audio channel of the video is a difficult task. Current state-of-the-art methods predict audio masks from artificially mixed spectrograms, known as Mi…
Audio Source SeparationDenoisingObjectSegmentation+3Compute and memory efficient universal sound source separation
Recent progress in audio source separation lead by deep learning has enabled many neural network models to provide robust solutions to this fundamental estimation problem. In this study, we provide a family of efficient …
Audio Source SeparationEfficient Neural NetworkSpeech SeparationMusic source separation conditioned on 3D point clouds
Recently, significant progress has been made in audio source separation by the application of deep learning techniques. Current methods that combine both audio and visual information use 2D representations such as images…
Audio Source SeparationMusic Source SeparationDirectional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech Mixtures
In blind source separation of speech signals, the inherent imbalance in the source spectrum poses a challenge for methods that rely on single-source dominance for the estimation of the mixing matrix. We propose an algori…
Audio Source Separationblind source separationMulti-Speaker Source SeparationSpeech SeparationDensely connected multidilated convolutional networks for dense prediction tasks
Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous …
Audio Source SeparationMusic Source SeparationSemantic SegmentationProblems using deep generative models for probabilistic audio source separation
Recent advancements in deep generative modeling make it possible to learn prior distributions from complex data that subsequently can be used for Bayesian inference. However, we find that distributions learned by deep ge…
Audio Source SeparationBayesian InferenceUnified Gradient Reweighting for Model Biasing with Applications to Source Separation
Recent deep learning approaches have shown great improvement in audio source separation tasks. However, the vast majority of such work is focused on improving average separation performance, often neglecting to examine o…
Audio Source SeparationA Study of Transfer Learning in Music Source Separation
Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data. While some music domains have enough data suitable for training a s…
Audio Source SeparationData AugmentationDeep LearningMusic Source Separation+1Towards Listening to 10 People Simultaneously: An Efficient Permutation Invariant Training of Audio Source Separation Using Sinkhorn's Algorithm
In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss. However, the ordinary PIT requires to try all $N!$ perm…
Audio Source SeparationSpeech SeparationFast accuracy estimation of deep learning based multi-class musical source separation
Music source separation represents the task of extracting all the instruments from a given song. Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB, only limited to four instrument clas…
Audio Source SeparationMusic Source SeparationThe Cone of Silence: Speech Separation by Localization
Given a multi-microphone recording of an unknown number of speakers talking concurrently, we simultaneously localize the sources and separate the individual speakers. At the core of our method is a deep network, in the w…
Audio Source SeparationSpeech SeparationAdversarial attacks on audio source separation
Despite the excellent performance of neural-network-based audio source separation methods and their wide range of applications, their robustness against intentional attacks has been largely neglected. In this work, we re…
Adversarial AttackAudio Source SeparationStructure and Automatic Segmentation of Dhrupad Vocal Bandish Audio
A Dhrupad vocal concert comprises a composition section that is interspersed with improvised episodes of increased rhythmic activity involving the interaction between the vocals and the percussion. Tracking the changing …
Audio Source SeparationAutoClip: Adaptive Gradient Clipping for Source Separation Networks
Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter. We present AutoClip, a simple method for automatically and adaptively choosing …
Audio Source SeparationSudo rm -rf: Efficient Networks for Universal Audio Source Separation
In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampli…
Audio Source SeparationEfficient Neural NetworkSpeech SeparationOtoWorld: Towards Learning to Separate by Learning to Move
We present OtoWorld, an interactive environment in which agents must learn to listen in order to solve navigational tasks. The purpose of OtoWorld is to facilitate reinforcement learning research in computer audition, wh…
Audio Source SeparationNavigateOpenAI GymUnsupervised Audio Source Separation using Generative Priors
State-of-the-art under-determined audio source separation systems rely on supervised end-end training of carefully tailored neural network architectures operating either in the time or the spectral domain. However, these…
Audio Source SeparationTime-Domain Audio Source Separation Based on Wave-U-Net Combined with Discrete Wavelet Transform
We propose a time-domain audio source separation method using down-sampling (DS) and up-sampling (US) layers based on a discrete wavelet transform (DWT). The proposed method is based on one of the state-of-the-art deep n…
Audio Source SeparationMusic Source SeparationCooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices
Augmented listening devices such as hearing aids often perform poorly in noisy and reverberant environments with many competing sound sources. Large distributed microphone arrays can improve performance, but data from re…
Audio Source Separation