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Target Sound Extraction
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Target Sound Extraction is the task of extracting a sound corresponding to a given class from an audio mixture. The audio mixture may contain background noise with a relatively low amplitude compared to the foreground mixture components. The choice of the sound class is provided as input to the model in form of a string, integer, or a one-hot encoding of the sound class.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Leveraging Audio-Only Data for Text-Queried Target Sound Extraction
Multichannel-to-Multichannel Target Sound Extraction Using Direction and Timestamp Clues
Language-Queried Target Sound Extraction Without Parallel Training Data
SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer
Cross-attention Inspired Selective State Space Models for Target Sound Extraction
Can all variations within the unified mask-based beamformer framework achieve identical peak extraction performance?
CATSE: A Context-Aware Framework for Causal Target Sound Extraction
CLAPSep: Leveraging Contrastive Pre-trained Model for Multi-Modal Query-Conditioned Target Sound Extraction
Online Similarity-and-Independence-Aware Beamformer for Low-latency Target Sound Extraction
Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables
DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction
Target Sound Extraction with Variable Cross-modality Clues
Real-Time Target Sound Extraction
SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning
Few-shot learning of new sound classes for target sound extraction
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