Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags

@article{Favory2021LearningCT,
  title={Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags},
  author={Xavier Favory and Konstantinos Drossos and Tuomas Virtanen and Xavier Serra},
  journal={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2021},
  pages={596-600}
}
  • Xavier Favory, K. Drossos, X. Serra
  • Published 27 October 2020
  • Computer Science
  • ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Self-supervised audio representation learning offers an attractive alternative for obtaining generic audio embeddings, capable to be employed into various downstream tasks. Published approaches that consider both audio and words/tags associated with audio do not employ text processing models that are capable to generalize to tags unknown during training. In this work we propose a method for learning audio representations using an audio autoencoder (AAE), a general word embed-dings model (WEM… 

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References

SHOWING 1-10 OF 26 REFERENCES
COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations
TLDR
The results are promising, sometimes in par with the state-of-the-art in the considered tasks and the embeddings produced with the method are well correlated with some acoustic descriptors.
Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings
TLDR
This paper investigates how L3-Net design choices impact the performance of downstream audio classifiers trained with these embeddings, and shows that audio-informed choices of input representation are important, and that using sufficient data for training the embedding is key.
Unsupervised Learning of Semantic Audio Representations
  • A. Jansen, M. Plakal, R. Saurous
  • Computer Science
    2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2018
TLDR
This work considers several class-agnostic semantic constraints that apply to unlabeled nonspeech audio and proposes low-dimensional embeddings of the input spectrograms that recover 41% and 84% of the performance of their fully-supervised counterparts when applied to downstream query-by-example sound retrieval and sound event classification tasks, respectively.
Semi-supervised Triplet Loss Based Learning of Ambient Audio Embeddings
TLDR
This paper combines unsupervised and supervised triplet loss based learning into a semi-supervised representation learning approach, whereby the positive samples for those triplets whose anchors are unlabeled are obtained either by applying a transformation to the anchor, or by selecting the nearest sample in the training set.
musicnn: Pre-trained convolutional neural networks for music audio tagging
TLDR
The musicnn library contains a set of pre-trained musically motivated convolutional neural networks for music audio tagging, which can be used as out-of-the-box music audio taggers, as music feature extractors, or as pre- trained models for transfer learning.
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
TLDR
Cross-Modal Deep Clustering (XDC), a novel self-supervised method that leverages unsupervised clustering in one modality as a supervisory signal for the other modality, is proposed, which is the first self- supervised learning method that outperforms large-scale fully- Supervised pretraining for action recognition on the same architecture.
Audio Set: An ontology and human-labeled dataset for audio events
TLDR
The creation of Audio Set is described, a large-scale dataset of manually-annotated audio events that endeavors to bridge the gap in data availability between image and audio research and substantially stimulate the development of high-performance audio event recognizers.
SampleCNN: End-to-End Deep Convolutional Neural Networks Using Very Small Filters for Music Classification
TLDR
A CNN architecture which learns representations using sample-level filters beyond typical frame-level input representations is proposed and extended using multi-level and multi-scale feature aggregation technique and subsequently conduct transfer learning for several music classification tasks.
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
TLDR
A powerful new WaveNet-style autoencoder model is detailed that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform, and NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets is introduced.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TLDR
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
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