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Sound Event Detection
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Sound Event Detection (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is referred as polyphonic SED.
Source: A report on sound event detection with different binaural features
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection
Exploring the Potential of SSL Models for Sound Event Detection
Temporal Attention Pooling for Frequency Dynamic Convolution in Sound Event Detection
Formula-Supervised Sound Event Detection: Pre-Training Without Real Data
Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models
Robust detection of overlapping bioacoustic sound events
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning
Synthetic data enables context-aware bioacoustic sound event detection
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event Detection
Towards Understanding of Frequency Dependence on Sound Event Detection
An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation
Pseudo Strong Labels from Frame-Level Predictions for Weakly Supervised Sound Event Detection
Leveraging LLM and Text-Queried Separation for Noise-Robust Sound Event Detection
Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection
Exploring Text-Queried Sound Event Detection with Audio Source Separation
The Sounds of Home: A Speech-Removed Residential Audio Dataset for Sound Event Detection
Effective Pre-Training of Audio Transformers for Sound Event Detection
Unified Audio Event Detection
Energy Consumption Trends in Sound Event Detection Systems
MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection
From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems
Impact of Noisy Labels on Sound Event Detection: Deletion Errors Are More Detrimental Than Insertion Errors
MAT-SED: A Masked Audio Transformer with Masked-Reconstruction Based Pre-training for Sound Event Detection
SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation
Multi-Iteration Multi-Stage Fine-Tuning of Transformers for Sound Event Detection with Heterogeneous Datasets
Improving Audio Spectrogram Transformers for Sound Event Detection Through Multi-Stage Training
Mixstyle based Domain Generalization for Sound Event Detection with Heterogeneous Training Data
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System
UCIL: An Unsupervised Class Incremental Learning Approach for Sound Event Detection
FMSG-JLESS Submission for DCASE 2024 Task4 on Sound Event Detection with Heterogeneous Training Dataset and Potentially Missing Labels
Self Training and Ensembling Frequency Dependent Networks with Coarse Prediction Pooling and Sound Event Bounding Boxes
Pushing the Limit of Sound Event Detection with Multi-Dilated Frequency Dynamic Convolution
Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data
Sound event detection based on auxiliary decoder and maximum probability aggregation for DCASE Challenge 2024 Task 4
DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels
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