Zobrazeno 1 - 10
of 12 209
pro vyhledávání: '"Politis AN"'
This paper investigates the feasibility of class-incremental learning (CIL) for Sound Event Localization and Detection (SELD) tasks. The method features an incremental learner that can learn new sound classes independently while preserving knowledge
Externí odkaz:
http://arxiv.org/abs/2411.12830
Autor:
Garcia-Martinez, Jaime, Diaz-Guerra, David, Politis, Archontis, Virtanen, Tuomas, Carabias-Orti, Julio J., Vera-Candeas, Pedro
Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks. These developments owe much to the creation and use of large-scale, multitrack datasets dedica
Externí odkaz:
http://arxiv.org/abs/2409.10995
Autor:
Wu, Kejin, Politis, Dimitris N.
In this paper, we provide a novel Model-free approach based on Deep Neural Network (DNN) to accomplish point prediction and prediction interval under a general regression setting. Usually, people rely on parametric or non-parametric models to bridge
Externí odkaz:
http://arxiv.org/abs/2408.09532
Publikováno v:
Transport and Telecommunication, Vol 21, Iss 1, Pp 61-68 (2020)
Full Duplex (FD) wireless communications is considered to be the next big step for future Wireless Local Area Networks (WLANs). Old (IEEE 802.11ac) and new (IEEE 802.11ax) WLAN features are expected to co-exist with FD operation. Some of these featur
Externí odkaz:
https://doaj.org/article/4439b4851c2b4d9b9dc156b29c5040df
Autor:
Politis, Archontis
Acoustical signal processing of directional representations of sound fields, including source, receiver, and scatterer transfer functions, are often expressed and modeled in the spherical harmonic domain (SHD). Certain such modeling operations, or ap
Externí odkaz:
http://arxiv.org/abs/2407.06847
Reference Channel Selection by Multi-Channel Masking for End-to-End Multi-Channel Speech Enhancement
In end-to-end multi-channel speech enhancement, the traditional approach of designating one microphone signal as the reference for processing may not always yield optimal results. The limitation is particularly in scenarios with large distributed mic
Externí odkaz:
http://arxiv.org/abs/2406.03228
Autor:
Wu, Kejin, Politis, Dimitris N.
Deep neural networks (DNN) has received increasing attention in machine learning applications in the last several years. Recently, a non-asymptotic error bound has been developed to measure the performance of the fully connected DNN estimator with Re
Externí odkaz:
http://arxiv.org/abs/2405.08276
Distance estimation from audio plays a crucial role in various applications, such as acoustic scene analysis, sound source localization, and room modeling. Most studies predominantly center on employing a classification approach, where distances are
Externí odkaz:
http://arxiv.org/abs/2403.17514
Sound Event Detection and Localization (SELD) is a combined task of identifying sound events and their corresponding direction-of-arrival (DOA). While this task has numerous applications and has been extensively researched in recent years, it fails t
Externí odkaz:
http://arxiv.org/abs/2403.11827
Scene-based spatial audio formats, such as Ambisonics, are playback system agnostic and may therefore be favoured for delivering immersive audio experiences to a wide range of (potentially unknown) devices. The number of channels required to deliver
Externí odkaz:
http://arxiv.org/abs/2401.13401