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pro vyhledávání: '"Labbé, Étienne"'
Automated Audio Captioning (AAC) systems attempt to generate a natural language sentence, a caption, that describes the content of an audio recording, in terms of sound events. Existing datasets provide audio-caption pairs, with captions written in E
Externí odkaz:
http://arxiv.org/abs/2309.07615
Automated Audio Captioning (AAC) involves generating natural language descriptions of audio content, using encoder-decoder architectures. An audio encoder produces audio embeddings fed to a decoder, usually a Transformer decoder, for caption generati
Externí odkaz:
http://arxiv.org/abs/2309.00454
Publikováno v:
DCASE2023, Sep 2023, Tampere, Finland
Automated Audio Captioning (AAC) aims to develop systems capable of describing an audio recording using a textual sentence. In contrast, Audio-Text Retrieval (ATR) systems seek to find the best matching audio recording(s) for a given textual query (T
Externí odkaz:
http://arxiv.org/abs/2308.15090
In computer vision, convolutional neural networks (CNN) such as ConvNeXt, have been able to surpass state-of-the-art transformers, partly thanks to depthwise separable convolutions (DSC). DSC, as an approximation of the regular convolution, has made
Externí odkaz:
http://arxiv.org/abs/2306.00830
In this work, we propose to study the performance of a model trained with a sentence embedding regression loss component for the Automated Audio Captioning task. This task aims to build systems that can describe audio content with a single sentence w
Externí odkaz:
http://arxiv.org/abs/2305.01482
Automatic Audio Captioning (AAC) is the task that aims to describe an audio signal using natural language. AAC systems take as input an audio signal and output a free-form text sentence, called a caption. Evaluating such systems is not trivial, since
Externí odkaz:
http://arxiv.org/abs/2211.08983
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, 2022
In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three other algorithms, called MixMatc
Externí odkaz:
http://arxiv.org/abs/2102.08183
Akademický článek
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Autor:
Labbé, Etienne, Attisano, Liliana, Jones, Kevin R., Kam, Angela, Morshead, Cindi M., van der Kooy, Derek
Publikováno v:
The Journal of Cell Biology, 2006 Oct 01. 175(1), 159-168.
Externí odkaz:
https://www.jstor.org/stable/4152142
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2000 Jul . 97(15), 8358-8363.
Externí odkaz:
https://www.jstor.org/stable/122905