SOTAVerified

Audio captioning

Audio Captioning is the task of describing audio using text. The general approach is to use an audio encoder to encode the audio (example: PANN, CAV-MAE), and to use a decoder (example: transformer) to generate the text. To judge the quality of audio captions, though machine translation metrics (BLEU, METEOR, ROUGE) and image captioning metrics (SPICE, CIDER) are used, they are not very well-suited. Attempts have been made to use pretrained language model based metrics such as Sentence-BERT.

Papers

Showing 8190 of 119 papers

TitleStatusHype
Efficient Audio Captioning Transformer with Patchout and Text Guidance0
Towards Generating Diverse Audio Captions via Adversarial Training0
Impact of visual assistance for automated audio captioning0
Diversity and bias in audio captioning datasets0
Investigations in Audio Captioning: Addressing Vocabulary Imbalance and Evaluating Suitability of Language-Centric Performance Metrics0
Exploring Train and Test-Time Augmentations for Audio-Language Learning0
Automated Audio Captioning via Fusion of Low- and High- Dimensional Features0
Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity0
Language-based Audio Retrieval Task in DCASE 2022 Challenge0
An investigation on selecting audio pre-trained models for audio captioning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VASTCIDEr0.78Unverified
2VALORCIDEr0.74Unverified
3MQ-CapSPIDEr0.52Unverified
4SLAM-AACSPIDEr0.52Unverified
5LAVCapSPIDEr0.52Unverified
6EnCLAP++-largeSPIDEr0.51Unverified
7AutoCapSPIDEr0.51Unverified
8LOAESPIDEr0.51Unverified
9EnCLAP++-baseSPIDEr0.5Unverified
10EnCLAP-largeSPIDEr0.5Unverified
#ModelMetricClaimedVerifiedStatus
1VASTCIDEr0.52Unverified
2VALORCIDEr0.42Unverified
3SLAM-AACSPIDEr0.33Unverified
4LOAESPIDEr0.33Unverified
5MQ-CapSPIDEr0.32Unverified
6EnsembleSPIDEr0.32Unverified
7Audio Flamingo (Pengi trainset)SPIDEr0.31Unverified
8Ensemble-RLSPIDEr0.3Unverified
9Qwen-AudioSPIDEr0.29Unverified
10EnsembleSPIDEr0.21Unverified