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Found 4 papers, 4 papers with code
Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors
This work proposes a frame-wise online/streaming end-to-end neural diarization (FS-EEND) method in a frame-in-frame-out fashion.
Fine-tune the pretrained ATST model for sound event detection
In this work, we study the fine-tuning method of the pretrained models for SED.
Ranked #1 on
Sound Event Detection
on DESED
(using extra training data)
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
In order to tackle both clip-level and frame-level tasks, this paper proposes Audio Teacher-Student Transformer (ATST), with a clip-level version (named ATST-Clip) and a frame-level version (named ATST-Frame), responsible for learning clip-level and frame-level representations, respectively.
Ranked #8 on
Audio Classification
on AudioSet
RCT: Random Consistency Training for Semi-supervised Sound Event Detection
Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency.
Ranked #5 on
Sound Event Detection
on DESED
