Towards Unified All-Neural Beamforming for Time and Frequency Domain Speech Separation

Recently, frequency domain all-neural beamforming methods have achieved remarkable progress for multichannel speech separation. In parallel, the integration of time domain network structure and beamforming also gains significant attention. This study proposes a novel all-neural beamforming method in time domain and makes an attempt to unify the all-neural beamforming pipelines for time domain and frequency domain multichannel speech separation. The proposed model consists of two modules: separation and beamforming. Both modules perform temporal-spectral-spatial modeling and are trained from end-to-end using a joint loss function. The novelty of this study lies in two folds. Firstly, a time domain directional feature conditioned on the direction of the target speaker is proposed, which can be jointly optimized within the time domain architecture to enhance target signal estimation. Secondly, an all-neural beamforming network in time domain is designed to refine the pre-separated results. This module features with parametric time-variant beamforming coefficient estimation, without explicitly following the derivation of optimal filters that may lead to an upper bound. The proposed method is evaluated on simulated reverberant overlapped speech data derived from the AISHELL-1 corpus. Experimental results demonstrate significant performance improvements over frequency domain state-of-the-arts, ideal magnitude masks and existing time domain neural beamforming methods

Long-term auto-correlation statistics based voice activity detection for strong noisy speech

This paper proposes a voice activity detection (VAD) algorithm based on a novel long-term metric. By assuming that the most significant difference between noisy speech and non-speech is the harmonicity of the noisy speech spectrum caused by human nature, the long-term auto-correlation statistics (LTACS) measure is designed to be shown as a powerful metric used in VAD. The LTACS measure is calculated among several successive frames around the concerned frame and it represents the significance of harmonics of the signal spectrum over a long term rather than a short term. A novel LTACS-based VAD algorithm is derived by jointly making use of the minimum operator to reduce non-speech variability and of then calculating variance to detect speech. Simulative comparisons with four standardized VAD algorithms (ETSI adaptive multi-rate option 1 and 2, ETSI advanced front-end and G.729 Annex B) as well as three former proposed VAD algorithms show that the proposed LTACS-based VAD algorithm achieves the best performance under all SNR conditions, especially in strong noisy environments (e.g., SNR is -5dB or -10dB). ? 2014 IEEE.EI

Traffic incident classification at intersections based on image sequences by HMM/SVM classifiers

With the development of modern intelligent transportation systems (ITS), automatic traffic incident detection with quick response and high accuracy becomes one of the most important issues, especially for metropolitan streets that are full of signaled intersections. In this paper, we present our up-to-date research outcomes of the traffic incident detection system, which makes use of the image sequences gathered from a typical urban intersection. Basic image signal processing was used to extract image difference information for traffic image database construction. Feature extraction algorithms were then discussed and compared including PCA, FFT, and hybrid analysis of DCT-FFT. Finally, multi-classification of traffic signal logics (East-West, West-East, South-North, North-South) and accidents were realized by HMM (Hidden Markov Model) and SVM (Support Vector Machine) respectively. Experimental results showed that the hybrid DCT-FFT method gives the best features, and classification performance of SVM is superior to HMM with limited training samples, where the correction rate is 100% for SVM and 91% for HMM.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000286990500011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Information SystemsComputer Science, Software EngineeringComputer Science, Theory & MethodsEngineering, Electrical & ElectronicSCI(E)EICPCI-S(ISTP)

PCA/ICA-based SVM for fall recognition using MEMS motion sensing data

This paper presents the progress towards a fall recognition algorithm based on MEMS motion sensing data. A Micro Inertial Measurement Unit (mu IMU) that is 66 mm x 20 mm x 20 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, and a Bluetooth module. It records human motion information, and the database of FALL and NORMAL is formed. We propose principal component analysis (PCA) for feature generation and independent component analysis (ICA) for feature extraction. Then, we use support vector machine (SVM) for training process. Experiments show that the process can classify falls and other normal motions successfully.Computer Science, Hardware & ArchitectureComputer Science, Information SystemsEngineering, Electrical & ElectronicEICPCI-S(ISTP)

A systematic review of self-neglect and its risk factors among community-dwelling older adults

10.1080/13607863.2020.1821168Aging & Mental Health25122179-219

“Jolly fat” or “sad fat”: a systematic review and meta-analysis of the association between obesity and depression among community-dwelling older adults

10.1080/13607863.2020.1857687Aging & Mental Health26113-2

Predictive salivary biomarkers for early diagnosis of periodontal diseases – current and future developments

Periodontal diseases are chronic diseases of oral cavity comprising of inflammatory conditions which effect the supporting structures of dentition. It is a multifactorial disease which is also known to be affected by genetic and environmental factors. However, some of the clinical parameters such as probing depth, attachment level, plaque index, bleeding on probing and radiographic assessment of alveolar bone are known to assess the severity of disease, although the disease activity is not measured. In the current scenario the salivary diagnostic markers for diagnosis of periodontal diseases have included the salivary enzymes, immunoglobulins, bacterial components or products, phenotypic markers such as epithelial markers. Also, saliva is a mirror of oral and systemic health and a valuable source to find out the physiological aspects of periodontal diseases. The present review thus highlights various salivary biomarkers which are quick, easy and reliable method for assessing and monitoring periodontal disease that improves and speeds treatment decisions and moves the field closer to individualized point-of-care diagnostics