Zobrazeno 1 - 10
of 29 054
pro vyhledávání: '"A. Slim"'
Training speech separation models in the supervised setting raises a permutation problem: finding the best assignation between the model predictions and the ground truth separated signals. This inherently ambiguous task is customarily solved using Pe
Externí odkaz:
http://arxiv.org/abs/2411.18497
Autor:
Etiabi, Yaya, Eldeeb, Eslam, Shehab, Mohammad, Njima, Wafa, Alves, Hirley, Alouini, Mohamed-Slim, Amhoud, El Mehdi
Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system leveraging sensor fusion, graph neural networks (GNNs), and meta-lear
Externí odkaz:
http://arxiv.org/abs/2411.17781
Autor:
Al-ZuBi, Muneer, Alouini, Mohamed-Slim
In this era of advanced communication technologies, many remote rural and hard-to-reach areas still lack Internet access due to technological, geographical, and economic challenges. The TV white space (TVWS) technology has proven to be effective and
Externí odkaz:
http://arxiv.org/abs/2411.13987
Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, may often impair users' privacy and demand significant on device computational resources. Centralized learning (CL) on the edge offers an alternativ
Externí odkaz:
http://arxiv.org/abs/2411.06291
Publikováno v:
ISMIR 2024, Nov 2024, San Francisco, Californ, United States
In this paper, we propose a novel Self-Supervised-Learning scheme to train rhythm analysis systems and instantiate it for few-shot beat tracking. Taking inspiration from the Contrastive Predictive Coding paradigm, we propose to train a Log-Mel-Spectr
Externí odkaz:
http://arxiv.org/abs/2411.04152
In this study, we statistically analyze the performance of a threshold-based multiple optical signal selection scheme (TMOS) for wavelength division multiplexing (WDM) and adaptive coded modulation (ACM) using free space optical (FSO) communication b
Externí odkaz:
http://arxiv.org/abs/2410.10335
Multimodal large language models have fueled progress in image captioning. These models, fine-tuned on vast image datasets, exhibit a deep understanding of semantic concepts. In this work, we show that this ability can be re-purposed for audio captio
Externí odkaz:
http://arxiv.org/abs/2410.05997
Audio-text models trained via contrastive learning offer a practical approach to perform audio classification through natural language prompts, such as "this is a sound of" followed by category names. In this work, we explore alternative prompt templ
Externí odkaz:
http://arxiv.org/abs/2409.13676
Publikováno v:
DCASE, Oct 2024, Tokyo, Japan
Machine listening systems often rely on fixed taxonomies to organize and label audio data, key for training and evaluating deep neural networks (DNNs) and other supervised algorithms. However, such taxonomies face significant constraints: they are co
Externí odkaz:
http://arxiv.org/abs/2409.11746
Autor:
Aldhaheri, Lameya, Alshehhi, Noor, Manzil, Irfana Ilyas Jameela, Khalil, Ruhul Amin, Javaid, Shumaila, Saeed, Nasir, Alouini, Mohamed-Slim
The emerging field of smart agriculture leverages the Internet of Things (IoT) to revolutionize farming practices. This paper investigates the transformative potential of Long Range (LoRa) technology as a key enabler of long-range wireless communicat
Externí odkaz:
http://arxiv.org/abs/2409.11200