26 accepted papers
Trajectory planning for autonomous driving in dynamic unstructured traffic remains a fundamental challenge. Existing methods are often reactive, i.e., they only respond to observed situations without explicitly anticipating future risks. Moreover, most reinforcement learning based approaches rely on…
Decoding speech from neural recordings has critical importance in application and scientific research. However, this task is still challenging with non-invasive recordings. Previous research has shown significant improvement in speech perception decoding task by leveraging wav2vec vectors and gives…
The Transformer model, particularly its cross-attention module, is widely used for feature fusion in target sound extraction which extracts the signal of interest based on given clues. Despite its effectiveness, this approach suffers from low computational efficiency. Recent advancements in state sp…
Modern robots must coexist with humans in dense urban environments. A key challenge is the ghost probe problem, where pedestrians or objects unexpectedly rush into traffic paths. This issue affects both autonomous vehicles and human drivers. Existing works propose vehicle-to-everything (V2X) strateg
Precision is a crucial performance indicator for robot arms. During interacting with human, high precision enables a robot arm to be used effectively and safely, while low precision may lead to safety issues. Traditional methods for improving robot arm precision rely on error compensation. However,
Trajectory prediction is a fundamental technology for advanced autonomous driving systems and represents one of the most challenging problems in the field of cognitive intelligence. Accurately predicting the future trajectories of each traffic participant is a prerequisite for building high safety a
As artificial intelligence-generated content (AIGC) continues to evolve, video-to-audio (V2A) generation has emerged as a key area with promising applications in multimedia editing, augmented reality, and automated content creation. While Transformer and Diffusion models have advanced audio generati…
Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements of electroencephalography (EEG) data. Most studies on AAD are based on scalp-EEG signals in two-speaker scenarios, which…
Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for…
The traditional feedback Active Noise Control (ANC) algorithms are built upon linear filters, which leads to reduced performance when dealing with real-world noise. Deep learning-based feedback ANC algorithms have been proposed to overcome this problem. However, methods relying on pre-trained neural…
Decoding language from neural signals holds considerable theoretical and practical importance. Previous research has indicated the feasibility of decoding text or speech from invasive neural signals. However, when using non-invasive neural signals, significant challenges are encountered due to their…
Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical he…
Monaural speech separation aims to separate concurrent speakers from a single-microphone mixture recording. Inspired by the effect of pitch priming in auditory scene analysis (ASA) mechanisms, a novel pitch-guided speech separation framework is proposed in this work. The prominent advantage of this…
In the current method for the sound field translation tasks based on spherical harmonic (SH) analysis, the solution based on the additive theorem usually faces the problem of singular values caused by large matrix condition numbers. The influence of different distances and frequencies of the spheric…
Pitch is a critical cue in human speech perception. Although the technology of tracking pitch in single-talker speech succeeds in many applications, it’s still a challenging problem to extract pitch information from mixtures. Inspired by the motor theory of speech perception, a novel multi-speaker p…
The lack of data is a major problem in individual HRTF modeling. There are many HRTF databases, but each database only has limited HRTFs with different characteristics, such as distance-dependent HRTFs or individual HRTFs. How to effectively model HRTFs through several different databases is an impo…
Pitch is a critical cue for speech separation in humans' auditory perception. Although the technology of tracking pitch in single-talker speech succeeds in many applications, it's still a challenging problem to extract pitch information from speech mixtures in machine perception. In this paper, we a…
Speech separation can be treated as a mask estimation problem where supervised learning is employed to construct the mapping from acoustic features to a mask. Interference can be reduced by applying the estimated mask on a time-frequency (T-F) representation of noisy speech, resulting in improved sp…
In this paper, a method for modeling distance dependent head-related transfer functions is presented. The HRTFs are first decomposed by spatial principal component analysis. Using deep neural networks, we model the spatial principal component weights of different distances. Then we realize the predi…
The Ambisonic technique has been widely used for sound field recording and reproduction recently. However, the basic Ambisonic decoding method will break down when the playback loudspeakers distribute unevenly. Various methods have been proposed to solve this problem. This paper introduces several i…
Speech separation, which has been a challenging task for decades, especially at low signal-to-noise ratios (SNRs), can be cast as a classification problem. In such adverse acoustic environment, extracting robust features from noisy mixtures is crucial for successful classification. In the past studi…
The speech intelligibility index (SII) has been widely used as an objective method of predicting speech intelligibility, but its traditional form is most effective predicting speech intelligibility scores under stationary noise but not more challenging conditions (e.g., competing noise interference)…
The basic Ambisonics decoding method will break down when the playback loudspeakers distribute unevenly. This paper proposes a modified Ambisonics method, the matching projection decoding method, for solving this problem. The matching projection decoding method is a kind of the greedy algorithm. It…
Protecting robot from broken of falling is always a challenge issue for a bipedal humanoid robot in dealing with various locomotion related tasks to serve human society, especially as the assigned tasks turns increasingly complicated and the corresponding real environment gets more and more complex.…
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical mo…
Long short-tem memory (LSTM) recurrent neural networks have been shown to give state-of-the-art performance on many speech recognition tasks. To achieve a further performance improvement, in this paper, maxout units are proposed to be integrated with the LSTM cells, considering those units have brou…