Home› Projects› ICASSP-2023-24-Papers

Python

ICASSP-2023-24-Papers

ICASSP 2023-2024 Papers: A complete collection of influential and exciting research papers from the ICASSP 2023-24 conferences. Explore the latest advancements in acoustics, speech and signal processing. Code included. Star the repository to support the advanc

by DmitryRyumin · GitHub

525

Stars

Forks

License

Created

Last commit

Category

Language

asr denoising domain-adaptationMITPython

View on GitHub

In plain words

Read influential research papers on acoustics and signal processing from recent conferences.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →

ICASSP-2023-24-Papers — GitHub preview card

📈 Star history

2026-07-202026-08-31

📈 Track ICASSP-2023-24-Papers

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime

Get email alerts →

📄 About

ICASSP-2023-24-Papers has 525 stars on GitHub. It has been forked 23 times. ICASSP-2023-24-Papers is written mainly in Python. It has been in active development since 2023. ICASSP-2023-24-Papers is available under the MIT license. Its main topics are asr, denoising, domain-adaptation, face-recognition.

❓ Frequently asked questions

What is ICASSP-2023-24-Papers?

Is ICASSP-2023-24-Papers open source?

ICASSP-2023-24-Papers is an open-source project. It is released under the MIT license.

Is ICASSP-2023-24-Papers free?

Yes. ICASSP-2023-24-Papers is free and open source — you can use, modify and self-host it.

What license does ICASSP-2023-24-Papers use?

ICASSP-2023-24-Papers is available under the MIT license.

What language is ICASSP-2023-24-Papers written in?

ICASSP-2023-24-Papers is written mainly in Python.

🏅 Maintainer of this project?

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=dmitryryumin-icassp-2023-24-papers)](https://olud.ai/project/dmitryryumin-icassp-2023-24-papers.html)

More badge options →

🧬 Shares DNA with🧬 View the DNA map →

pytorch-adapt

Domain adaptation made easy. Fully featured, modular, and customizable.

★ 397 · computer-vision

sharesdomain-adaptation

pykale

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for…

★ 486 · computer-vision

sharesdomain-adaptation

Exclusively-Dark-Image-Dataset

Exclusively Dark (ExDARK) dataset which to the best of our knowledge, is the largest collection…

★ 635 · computer-vision

sharesdomain-adaptation

powerful-benchmarker

A library for ML benchmarking. It's powerful.

★ 441 · benchmarking

sharesdomain-adaptation

bonito

A lightweight library for generating synthetic instruction tuning datasets for your data withou…

★ 830 · domain-adaptation

sharesdomain-adaptation

openface

Face recognition with deep neural networks.

★ 15.4k · deep-learning

sharesface-recognition

Awesome-Diffusion-Models-in-Medical-Imaging

Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)

★ 2.1k · ddpm

sharesdenoising

facenet

Face recognition using Tensorflow

★ 14.3k · computer-vision

sharesface-recognition

AdaptSegNet

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

★ 859 · adversarial-learning

sharesdomain-adaptation

face_recognition

The world's simplest facial recognition api for Python and the command line

★ 56.6k · face-detection

sharesface-recognition

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.