1 code implementation • 10 Sep 2024 • Yizhou Tan, Yanru Wu, Yuanbo Hou, Xin Xu, Hui Bu, Shengchen Li, Dick Botteldooren, Mark D. Plumbley
By comparing human annotations with the predictions of ensemble pre-trained models, this paper uncovers a significant gap between human perception and model inference in both semantic identification and existence detection of audio events.
no code implementations • 26 Jun 2024 • Zhongju Yuan, Wannes Van Ransbeeck, Geraint Wiggins, Dick Botteldooren
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.
1 code implementation • 9 Jun 2024 • Yuanbo Hou, Qiaoqiao Ren, Andrew Mitchell, Wenwu Wang, Jian Kang, Tony Belpaeme, Dick Botteldooren
The average score (out of 5) of SoundSCaper-generated captions is lower than the score of captions generated by two soundscape experts by 0. 21 and 0. 25, respectively, on the evaluation set and the model-unknown mixed external dataset with varying lengths and acoustic properties, but the differences are not statistically significant.
no code implementations • 16 May 2024 • Zhongju Yuan, Geraint Wiggins, Dick Botteldooren
Leveraging reservoir computing, our proposed method is ultimately oriented towards predicting human perception of rhythm.
1 code implementation • 15 May 2024 • Qiaoqiao Ren, Yuanbo Hou, Dick Botteldooren, Tony Belpaeme
For this, the robot needs to know how difficult it is for a user to understand spoken language in a particular setting.
no code implementations • 21 Mar 2024 • Pengfei Sun, Jorg De Winne, Paul Devos, Dick Botteldooren
Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces.
1 code implementation • 15 Dec 2023 • Yuanbo Hou, Qiaoqiao Ren, Siyang Song, Yuxin Song, Wenwu Wang, Dick Botteldooren
Specifically, this paper proposes a lightweight multi-level graph learning (MLGL) based on local and global semantic graphs to simultaneously perform audio event classification (AEC) and human annoyance rating prediction (ARP).
1 code implementation • 15 Nov 2023 • Yuanbo Hou, Qiaoqiao Ren, Huizhong Zhang, Andrew Mitchell, Francesco Aletta, Jian Kang, Dick Botteldooren
(4) Generalization tests show that the proposed model's ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on sound-scape augmentation.
no code implementations • 23 Oct 2023 • Pengfei Sun, Jibin Wu, Malu Zhang, Paul Devos, Dick Botteldooren
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.
1 code implementation • 5 Oct 2023 • Yuanbo Hou, Siyang Song, Chuang Yu, Wenwu Wang, Dick Botteldooren
The results show the feasibility of recognizing diverse acoustic scenes based on the audio event-relational graph.
Acoustic Scene Classification Graph Representation Learning +1
1 code implementation • 23 Aug 2023 • Yuanbo Hou, Siyang Song, Cheng Luo, Andrew Mitchell, Qiaoqiao Ren, Weicheng Xie, Jian Kang, Wenwu Wang, Dick Botteldooren
Sound events in daily life carry rich information about the objective world.
no code implementations • 16 Feb 2023 • Pengfei Sun, Ehsan Eqlimi, Yansong Chua, Paul Devos, Dick Botteldooren
Spiking neural networks (SNN) are a promising research avenue for building accurate and efficient automatic speech recognition systems.
Ranked #3 on Audio Classification on SHD
1 code implementation • 27 Oct 2022 • Yuanbo Hou, Siyang Song, Chuang Yu, Yuxin Song, Wenwu Wang, Dick Botteldooren
Experiments on a polyphonic acoustic scene dataset show that the proposed ERGL achieves competitive performance on ASC by using only a limited number of embeddings of audio events without any data augmentations.
Acoustic Scene Classification Graph Representation Learning +1
no code implementations • 20 Apr 2022 • Pengfei Sun, Longwei Zhu, Dick Botteldooren
The information of spiking neural networks (SNNs) are propagated between the adjacent biological neuron by spikes, which provides a computing paradigm with the promise of simulating the human brain.
1 code implementation • 27 Oct 2020 • Yuanbo Hou, Yi Deng, Bilei Zhu, Zejun Ma, Dick Botteldooren
Detecting anchor's voice in live musical streams is an important preprocessing for music and speech signal processing.
Sound Multimedia Audio and Speech Processing