Papers Audio Source Separation
“Audio Source Separation” 태그가 달린 논문 117편 · 필터 해제
On loss functions and evaluation metrics for music source separation
We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation. To that end, we first survey the most representative audio source separation losses w…
Audio Source SeparationBenchmarkingMusic Source SeparationDifferentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds
A differentiable digital signal processing (DDSP) autoencoder is a musical sound synthesizer that combines a deep neural network (DNN) and spectral modeling synthesis. It allows us to flexibly edit sounds by changing the…
Audio Source SeparationUnsupervised Music Source Separation Using Differentiable Parametric Source Models
Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets …
Audio Source SeparationDeep LearningMusic Source SeparationVocal ensemble separationFish sounds: towards the evaluation of marine acoustic biodiversity through data-driven audio source separation
The marine ecosystem is changing at an alarming rate, exhibiting biodiversity loss and the migration of tropical species to temperate basins. Monitoring the underwater environments and their inhabitants is of fundamental…
Audio Source SeparationSelf-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning
Annotating musical beats is a very long and tedious process. In order to combat this problem, we present a new self-supervised learning pretext task for beat tracking and downbeat estimation. This task makes use of Splee…
Audio Source SeparationBeat TrackingContrastive LearningSelf-Supervised LearningZero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data
Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of audio sources. Although some universal se…
Audio Source SeparationAudio TaggingEvent DetectionSound Event Detection+1Zero-shot Audio Source Separation through Query-based Learningfrom Weakly-labeled Data
Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of audio sources. Although some universal se…
Audio Source SeparationEvent DetectionSound Event DetectionZero-shot GeneralizationHybrid Neural Networks for On-device Directional Hearing
On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements. While neural nets can achieve significantly better performance tha…
Audio Source SeparationCausal InferenceDirectional HearingReal-time Directional HearingTransfer Learning with Jukebox for Music Source Separation
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel. Our neural network architecture, which is usin…
Audio Source SeparationMusic Source SeparationTransfer LearningReduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks
Listening to the audio of TV broadcast signals can be challenging for hearing-impaired as well as normal-hearing listeners, especially when background sounds are prominent or too loud compared to the speech signal. This …
Audio Source SeparationSpeech EnhancementUnsupervised Source Separation By Steering Pretrained Music Models
We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining. An audio generation model is conditioned on an input mixt…
Audio GenerationAudio Source SeparationMusic GenerationMusic Tagging+1The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mainly focused on separating speech from no…
Audio Source SeparationUnsupervised Source Separation via Bayesian Inference in the Latent Domain
State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources. On the other hand, approaches for training these models without any direct…
Audio Source SeparationBayesian InferenceMusic Source SeparationVisual Scene Graphs for Audio Source Separation
State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sou…
Audio Source SeparationVisually Guided Sound Source SeparationMulti-Task Audio Source Separation
The audio source separation tasks, such as speech enhancement, speech separation, and music source separation, have achieved impressive performance in recent studies. The powerful modeling capabilities of deep neural net…
Audio Source SeparationMulti-task Audio Source SeperationMusic Source SeparationSpeech Enhancement+1Densely Connected Multi-Dilated 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 simultane…
Audio Source SeparationSemantic SegmentationParallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
This paper introduces an alternative approach to sampling from autoregressive models. Autoregressive models are typically sampled sequentially, according to the transition dynamics defined by the model. Instead, we propo…
Audio Source SeparationSuper-ResolutionMove2Hear: Active Audio-Visual Source Separation
We introduce the active audio-visual source separation problem, where an agent must move intelligently in order to better isolate the sounds coming from an object of interest in its environment. The agent hears multiple …
Audio Source SeparationObjectSampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method
Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the varieties of audio signals. Since samplin…
Audio Source SeparationMusic Source SeparationMULTIMODAL ANALYSIS: Informed content estimation and audio source separation
This dissertation proposes the study of multimodal learning in the context of musical signals. Throughout, we focus on the interaction between audio signals and text information. Among the many text sources related to mu…
Audio Source Separation