Audioclip: Extending Clip to Image, Text and Audio
Abstract
The rapidly evolving field of sound classification has greatly benefited from the methods of other domains. Today, the trend is to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models.We present AudioCLIP – an extension of the CLIP model that handles audio in addition to text and images. Utilizing the AudioSet dataset, our proposed model incorporates the ESResNeXt audio-model into the CLIP framework, thus enabling it to perform multimodal classification and keeping CLIP’s zero-shot capabilities.AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task and out-performs others by reaching accuracies of 97.15 % on ESC-50 and 90.07 % on UrbanSound8K. Further, it sets new baselines in the zero-shot ESC-task on the same datasets (69.40 % and 68.78 %, respectively).We also asses the influence of different training setups on the final performance of the proposed model. For the sake of reproducibility, our code is published.
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LTU-AS directly outputs predictions in natural language.
“Compared with CLIP-like models [48,47], LTU-AS does not require any pre-defined label set and directly outputs predictions in natural language”
From Joint Audio and Speech Understanding (2023) · cites this paper as [47]
LTU-AS does not require any pre-defined label set.
“Compared with CLIP-like models [48,47], LTU-AS does not require any pre-defined label set and directly outputs predictions in natural language”
From Joint Audio and Speech Understanding (2023) · cites this paper as [47]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
AST: Audio Spectrogram Transformer
Gong, Y., Chung, Y.-A., & Glass, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-698 · Cited by 1,159 · see citing works
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ImageBind One Embedding Space to Bind Them All
Girdhar, R., El-Nouby, A., Liu, Z., Singh, M., Alwala, K. V., Joulin, A., & Misra, I. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01457 · Cited by 863 · see citing works
Wu, Y., Chen, K., Zhang, T., Hui, Y., Berg-Kirkpatrick, T., & Dubnov, S. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10095969 · Cited by 459 · see citing works
“Nonetheless, current studies have not shown the full strength of the language-audio contrastive learning.”
BackgroundMethodFoundation Models Defining a New Era in Vision: A Survey and Outlook
Awais, M., Naseer, M., Khan, S., Anwer, R. M., Cholakkal, H., Shah, M., Yang, M.-H., & Khan, F. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3506283 · Cited by 304 · see citing works
ConceptFusion: Open-set multimodal 3D mapping
Jatavallabhula, K., Kuwajerwala, A., Gu, Q., Omama, M., Iyer, G., Saryazdi, S., Chen, T., Maalouf, A., Li, S., Keetha, N., et al. (2023) · Robotics: Science and Systems XIX · doi:10.15607/rss.2023.xix.066 · Cited by 228 · see citing works
Mei, X., Meng, C., Liu, H., Kong, Q., Ko, T., Zhao, C., Plumbley, M. D., Zou, Y., & Wang, W. (2024) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2024.3419446 · Cited by 150 · see citing works
COCOA: Cross Modality Contrastive Learning for Sensor Data
Deldari, S., Xue, H., Saeed, A., Smith, D. V., & Salim, F. D. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3550316 · Cited by 92 · see citing works
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3729467 · Cited by 33 · see citing works
“Looking beyond works in re-purposing language and vision foundation models for human activity recognition, significant efforts have been dedicated by researchers to developing foundational models for time-series data.”
Learning Music Sequence Representation From Text Supervision
Chen, T., Xie, Y., Zhang, S., Huang, S., Zhou, H., & Li, J. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746131 · Cited by 8 · see citing works
Joint Audio and Speech Understanding
Gong, Y., Liu, A. H., Luo, H., Karlinsky, L., & Glass, J. (2023) · arXiv · doi:10.48550/arxiv.2309.14405
“In addition, in the first and second stages, we only train LTU-AS with AQAs of classification tasks where the model gets a high penalty for wrong predictions.”
MethodHaresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Ploetz, T. (2024) · arXiv · Cited by 31 · see citing works
“Looking beyond works in re-purposing language and vision foundation models for human activity recognition, significant efforts have been dedicated by researchers to developing foundational models for time-series data.”
- Authors
- Guzhov, A., Raue, F., Hees, J., & Dengel, A.
- Affiliations
- German Research Centre for Artificial Intelligence (DE)
- Venue
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2022-05-23
- Pages
- 976-980
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3176445421
- Cited by
- 330
- References
- 4
- Citations by year
- 2026: 55 · 2025: 106 · 2024: 93 · 2023: 57 · 2022: 19
- FWCI
- 48.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- CLIP, audio classification, environmental sound classification, zero-shot learning, multimodal classification, AudioSet, UrbanSound8K, ESC-50
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