Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Botteldooren, Dick"'
In exploring the simulation of human rhythmic perception and synchronization capabilities, this study introduces a computational model inspired by the physical and biological processes underlying rhythm processing. Utilizing a reservoir computing fra
Externí odkaz:
http://arxiv.org/abs/2407.09538
Autor:
Hou, Yuanbo, Ren, Qiaoqiao, Mitchell, Andrew, Wang, Wenwu, Kang, Jian, Belpaeme, Tony, Botteldooren, Dick
We live in a rich and varied acoustic world, which is experienced by individuals or communities as a soundscape. Computational auditory scene analysis, disentangling acoustic scenes by detecting and classifying events, focuses on objective attributes
Externí odkaz:
http://arxiv.org/abs/2406.05914
This paper introduces a novel approach to predicting periodic time series using reservoir computing. The model is tailored to deliver precise forecasts of rhythms, a crucial aspect for tasks such as generating musical rhythm. Leveraging reservoir com
Externí odkaz:
http://arxiv.org/abs/2405.10102
Spoken language interaction is at the heart of interpersonal communication, and people flexibly adapt their speech to different individuals and environments. It is surprising that robots, and by extension other digital devices, are not equipped to ad
Externí odkaz:
http://arxiv.org/abs/2405.09708
Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advanc
Externí odkaz:
http://arxiv.org/abs/2403.15489
WHO's report on environmental noise estimates that 22 M people suffer from chronic annoyance related to noise caused by audio events (AEs) from various sources. Annoyance may lead to health issues and adverse effects on metabolic and cognitive system
Externí odkaz:
http://arxiv.org/abs/2312.09952
Autor:
Hou, Yuanbo, Ren, Qiaoqiao, Zhang, Huizhong, Mitchell, Andrew, Aletta, Francesco, Kang, Jian, Botteldooren, Dick
Publikováno v:
The Journal of the Acoustical Society of America, 154, 3145 (2023)
Soundscape studies typically attempt to capture the perception and understanding of sonic environments by surveying users. However, for long-term monitoring or assessing interventions, sound-signal-based approaches are required. To this end, most pre
Externí odkaz:
http://arxiv.org/abs/2311.09030
Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing. Nevertheless, vanilla RNNs are confronted with the well-known issue
Externí odkaz:
http://arxiv.org/abs/2310.14982
Most deep learning-based acoustic scene classification (ASC) approaches identify scenes based on acoustic features converted from audio clips containing mixed information entangled by polyphonic audio events (AEs). However, these approaches have diff
Externí odkaz:
http://arxiv.org/abs/2310.03889
Autor:
Hou, Yuanbo, Song, Siyang, Luo, Cheng, Mitchell, Andrew, Ren, Qiaoqiao, Xie, Weicheng, Kang, Jian, Wang, Wenwu, Botteldooren, Dick
Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches only focus on classifying and detecting audio events and scenes, but may
Externí odkaz:
http://arxiv.org/abs/2308.11980