20 accepted papers
Radar technology offers much appeal for contactless vital sign monitoring. While most radar-based approaches achieve reasonable performance for single-person scenarios, they suffer from inaccurate vital sign estimates for multiple individuals, especially when the subjects occupy the same range bin.…
We present SMARTMiner, a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching (HC) notes. Developed in response to challenges observed during a clinical trial, the SMARTMiner achieves two tasks: (i) extracting
The sparsity of point clouds and inadequacy of semantic information pose challenges to current LiDAR-only 3D object detection methods. Recent methods alleviate these challenges by converting RGB images into virtual points via depth completion to be fused with LiDAR points. Although these methods hav…
Languages usually switch within a multilingual speech signal, especially in a bilingual society. This phenomenon is referred to as code-switching (CS), making automatic speech recognition (ASR) challenging under a multilingual scenario. We propose to improve CS-ASR by biasing the hybrid CTC/attentio…
Packet loss concealment (PLC) is a tool for enhancing speech degradation caused by poor network conditions or underflow/overflow in audio processing pipelines. We propose a real-time recurrent method that leverages previous outputs to mitigate artefact of lost packets without the prior knowledge of…
Code-switching (CS) occurs when languages switch within a speech signal and leads to language confusion for automatic speech recognition (ASR). We address the problem of language confusion for improving CS-ASR from two perspectives: incorporating and disentangling language information. We incorporat…
This work addresses the source localization and association problem in a multipath propagation environment. By focusing on the limitation of the prior information in practical applications, we propose a target localization and association method based on iterative optimization with semi-unitary cons…
Pre-trained single-channel neural networks have become more prevalent for noise reduction in recent years. However, unlike their multichannel counterparts, these monoaural approaches do not exploit spatial information during the optimization process. Furthermore, while multichannel neural networks e…
We introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension. The proposed architecture simplifies the UNet backbone of the TFiLM to reduce inference time and employs an efficient transformer at the bottleneck to alleviate performanc…
Virtual microphone beamforming techniques are attractive for devices limited by space constraints. These techniques synthesize virtual microphone signals via interpolation algorithms. We propose to extend existing virtual microphone signal interpolation by employing an adaptive non-linear (ANL) proc…
In blind source separation of speech signals, the inherent imbalance in the source spectrum poses a challenge for methods that rely on single-source dominance for the estimation of the mixing matrix. We propose an algorithm based on the directional sparse filtering (DSF) framework that utilizes the…
Complex-valued independent component analysis (ICA) is a celebrated method in blind separation of complex-valued signals. In this paper, we propose to transform the constrained optimization problems of complex-valued ICA into unconstrained optimization problems which can be solved by limited-memory…
To deliver on the potential outcome-based teaching and learning holds for engineering education, it is important for engineering courses to provide students with different types of deliberate practice opportunities that align to the program's learning outcomes. Working from these requirements, we in…
Impact of online learning sequences to forecast course outcomes for an undergraduate digital signal processing (DSP) course is studied in this work. A multi-modal learning schema based on deep-learning techniques with learning sequences, psychometric measures, and personality traits as input feature…
We propose a new sparse coding technique based on the power mean of phase-invariant cosine distances. Our approach is a generalization of sparse filtering and K-hyperlines clustering. It offers a better sparsity enforcer than the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="htt…
We propose a TDOA-based algorithm for source localization on rigid surfaces. This allows the conversion of readily available large surfaces into touch interfaces using surface-mounted vibration sensors. To achieve this, we characterize the arrival of each sensor-received signal by the arrival times…
We propose a new algorithm for source localization on rigid surfaces, which allows one to convert daily objects into human-computer touch interfaces using surface-mounted vibration sensors. This is achieved via estimating the time-difference-of-arrivals (TDOA) of the signals across the sensors. In t…
Due to practical considerations the microphone spacing is increased to achieve improved resolution by violating the spatial Nyquist criterion. Accompanied aliasing components adversely affect the identifiability of the source direction peaks. We investigate the effect of aliasing on the spatial spec…
We address the problem of estimating direction-of-arrivals (DOAs) for multiple sound sources using a single acoustic vector sensor (AVS) in an enclosed room environment. It is well-known that multi-source DOA estimation in an enclosed environment is challenging due to room reverberation, environment…
This paper focuses on the problem of single-channel noise reduction in a transient noise environment for speech enhancement application. A typical speech enhancement algorithm requires an estimate of the noise statistics. However, the problem of noise estimation is challenging when the statistics of…