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 76–100 of 119 papers

TitleStatusHype
Audio Difference Captioning Utilizing Similarity-Discrepancy DisentanglementCode0
Rethinking Transfer and Auxiliary Learning for Improving Audio Captioning Transformer—0
Crowdsourcing and Evaluating Text-Based Audio Retrieval RelevancesCode0
Improving Audio Caption Fluency with Automatic Error Correction—0
Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning—0
Efficient Audio Captioning Transformer with Patchout and Text Guidance—0
Towards Generating Diverse Audio Captions via Adversarial Training—0
Impact of visual assistance for automated audio captioning—0
Diversity and bias in audio captioning datasets—0
Investigations in Audio Captioning: Addressing Vocabulary Imbalance and Evaluating Suitability of Language-Centric Performance Metrics—0
Exploring Train and Test-Time Augmentations for Audio-Language Learning—0
Automated Audio Captioning via Fusion of Low- and High- Dimensional Features—0
Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity—0
Language-based Audio Retrieval Task in DCASE 2022 Challenge—0
An investigation on selecting audio pre-trained models for audio captioning—0
Automated Audio Captioning and Language-Based Audio RetrievalCode0
Language-based Audio Retrieval Task in DCASE 2022 ChallengeCode0
Automated Audio Captioning with Epochal Difficult Captions for Curriculum Learning—0
Automated Audio Captioning: An Overview of Recent Progress and New Challenges—0
Caption Feature Space Regularization for Audio CaptioningCode0
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning—0
Leveraging Pre-trained BERT for Audio Captioning—0
Joint Speech Recognition and Audio Captioning—0
Automatic Audio Captioning using Attention weighted Event based Embeddings—0
Local Information Assisted Attention-free Decoder for Audio CaptioningCode0
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Benchmark Results

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