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 7180 of 119 papers

TitleStatusHype
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer0
Classifier-Guided Captioning Across Modalities0
CosyAudio: Improving Audio Generation with Confidence Scores and Synthetic Captions0
Diverse Audio Captioning via Adversarial Training0
Diversity and bias in audio captioning datasets0
Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning0
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning0
Efficient Audio Captioning Transformer with Patchout and Text Guidance0
EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation0
Enhancing Low-Resource Language and Instruction Following Capabilities of Audio Language Models0
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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