#icassp — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #icassp, aggregated by home.social.
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#GRETSI2025, #EUSIPCO2025 sont terminés, la soumission à #ICASSP révolue, il fait gris. Abonner-vous à la chaîne YouTube #GRETSI @gretsi6095, et visionnez par exemple Lenka Zdeborova (Professeur EPFL), "Apprentissage (profond) et Physique Statistique"
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#GRETSI2025, #EUSIPCO2025 sont terminés, la soumission à #ICASSP révolue, il fait gris. Abonner-vous à la chaîne YouTube #GRETSI @gretsi6095, et visionnez par exemple Lenka Zdeborova (Professeur EPFL), "Apprentissage (profond) et Physique Statistique"
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We have 2 research papers accepted to present at #ICASSP in Hyderabad! You can read the preprints:
"LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging" led by Shubhr Singh https://arxiv.org/abs/2501.03464
"Acoustic identification of individual animals with hierarchical contrastive learning" led by Ines Nolasco https://arxiv.org/abs/2409.08673 #machinelearning #machinelistening #bioacoustics -
We have 2 research papers accepted to present at #ICASSP in Hyderabad! You can read the preprints:
"LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging" led by Shubhr Singh https://arxiv.org/abs/2501.03464
"Acoustic identification of individual animals with hierarchical contrastive learning" led by Ines Nolasco https://arxiv.org/abs/2409.08673 #machinelearning #machinelistening #bioacoustics -
We have 2 research papers accepted to present at #ICASSP in Hyderabad! You can read the preprints:
"LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging" led by Shubhr Singh https://arxiv.org/abs/2501.03464
"Acoustic identification of individual animals with hierarchical contrastive learning" led by Ines Nolasco https://arxiv.org/abs/2409.08673 #machinelearning #machinelistening #bioacoustics -
We have 2 research papers accepted to present at #ICASSP in Hyderabad! You can read the preprints:
"LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging" led by Shubhr Singh https://arxiv.org/abs/2501.03464
"Acoustic identification of individual animals with hierarchical contrastive learning" led by Ines Nolasco https://arxiv.org/abs/2409.08673 #machinelearning #machinelistening #bioacoustics -
We have 2 research papers accepted to present at #ICASSP in Hyderabad! You can read the preprints:
"LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging" led by Shubhr Singh https://arxiv.org/abs/2501.03464
"Acoustic identification of individual animals with hierarchical contrastive learning" led by Ines Nolasco https://arxiv.org/abs/2409.08673 #machinelearning #machinelistening #bioacoustics -
they can’t keep getting away with it #icassp #interspeech
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they can’t keep getting away with it #icassp #interspeech
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they can’t keep getting away with it #icassp #interspeech
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they can’t keep getting away with it #icassp #interspeech
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they can’t keep getting away with it #icassp #interspeech
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you do in fact love to see it (and that this is now, in fact, the standard) #icassp #icassp2024
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you do in fact love to see it (and that this is now, in fact, the standard) #icassp #icassp2024
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you do in fact love to see it (and that this is now, in fact, the standard) #icassp #icassp2024
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you do in fact love to see it (and that this is now, in fact, the standard) #icassp #icassp2024
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you do in fact love to see it (and that this is now, in fact, the standard) #icassp #icassp2024
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this #icassp industry talk could have been a Well There’s Your Problem podcast episode
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this #icassp industry talk could have been a Well There’s Your Problem podcast episode
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this #icassp industry talk could have been a Well There’s Your Problem podcast episode
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this #icassp industry talk could have been a Well There’s Your Problem podcast episode
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this #icassp industry talk could have been a Well There’s Your Problem podcast episode
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Our pick of the week by Dennis Fucci: "Explanations for Automatic Speech Recognition" (Wu et al., 2023 #ICASSP).
https://ieeexplore.ieee.org/document/10094635
#NLProc #NLP #Speech #Recognition #ASR #SpeechRecognition #explanation #explainableAI #AI
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And with an automated publication of pyclarity-0.2.0 to #PyPI my work for today is done.
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And with an automated publication of pyclarity-0.2.0 to #PyPI my work for today is done.
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Here is a machine learning challenge: "Decode" EEG to estimate what a listener was hearing, actually, what features of continuous speech can be predicted from EEG.
https://exporl.github.io/auditory-eeg-challenge-2023/
The challenge runs from now until February 6, 2023, after which the top 5 teams will be invited to submit a 2-page paper to ICASSP and later on an invitation to write a journal paper for the IEEE open journal of signal processing.
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Here is a machine learning challenge: "Decode" EEG to estimate what a listener was hearing, actually, what features of continuous speech can be predicted from EEG.
https://exporl.github.io/auditory-eeg-challenge-2023/
The challenge runs from now until February 6, 2023, after which the top 5 teams will be invited to submit a 2-page paper to ICASSP and later on an invitation to write a journal paper for the IEEE open journal of signal processing.
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Here is a machine learning challenge: "Decode" EEG to estimate what a listener was hearing, actually, what features of continuous speech can be predicted from EEG.
https://exporl.github.io/auditory-eeg-challenge-2023/
The challenge runs from now until February 6, 2023, after which the top 5 teams will be invited to submit a 2-page paper to ICASSP and later on an invitation to write a journal paper for the IEEE open journal of signal processing.
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Here is a machine learning challenge: "Decode" EEG to estimate what a listener was hearing, actually, what features of continuous speech can be predicted from EEG.
https://exporl.github.io/auditory-eeg-challenge-2023/
The challenge runs from now until February 6, 2023, after which the top 5 teams will be invited to submit a 2-page paper to ICASSP and later on an invitation to write a journal paper for the IEEE open journal of signal processing.
