Multi-modal Blind Source Separation with Microphones and Blinkies

We propose a blind source separation algorithm that jointly exploits measurements by a conventional microphone array and an ad hoc array of low-rate sound power sensors called blinkies. While providing less information than microphones, blinkies circumvent some difficulties of microphone arrays in terms of manufacturing, synchronization, and deployment. The algorithm is derived from a joint probabilistic model of the microphone and sound power measurements. We assume the separated sources to follow a time-varying spherical Gaussian distribution, and the non-negative power measurement space-time matrix to have a low-rank structure. We show that alternating updates similar to those of independent vector analysis and Itakura-Saito non-negative matrix factorization decrease the negative log-likelihood of the joint distribution. The proposed algorithm is validated via numerical experiments. Its median separation performance is found to be up to 8 dB more than that of independent vector analysis, with significantly reduced variability.Comment: Accepted at IEEE ICASSP 2019, Brighton, UK. 5 pages. 3 figure

Multi-user Communication in Difficult Interference

The co-channel interference (CCI) is one of the major impairments in wireless communication. CCI typically reduces the reliability of wireless communication links, but the difficult CCI which is no more or less strong to the desired signals destroys wireless links despite having myriad of CCI mitigation methods. It is shown in this paper that M-QAM (Quadrature Amplitude Modulation) or similar modulation schemes which modulate information both in in-phase and quadrature-phase are particularly vulnerable to difficult CCI. Despite well-known shortcomings, it is shown in this paper that M-PAM or similar schemes that use a single dimension for modulation provides an important mean for difficult CCI mitigation.Comment: 4 pages, 2 figs and accepted in IEEE ICASSP 2019, Brighton, U

Energy Disaggregation Using Elastic Matching Algorithms

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio

Cross-language Speech Dependent Lip-synchronization

Understanding videos of people speaking across international borders is hard as audiences from different demographies do not understand the language. Such speech videos are often supplemented with language subtitles. However, these hamper the viewing experience as the attention is shared. Simple audio dubbing in a different language makes the video appear unnatural due to unsynchronized lip motion. In this paper, we propose a system for automated cross-language lip synchronization for re-dubbed videos. Our model generates superior photorealistic lip-synchronization over original video in comparison to the current re-dubbing method. With the help of a user-based study, we verify that our method is preferred over unsynchronized videos.</p

Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah

Wi-Fi systems based on the IEEE 802.11 standards are the most popular wireless interfaces that use Listen Before Talk (LBT) method for channel access. The distinctive feature of a majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to perform packet detection and carrier frequency offset (CFO) estimation. Preambles usually contain repetitions of training symbols with good correlation properties, while conventional digital receivers apply correlation-based methods for both packet detection and CFO estimation. However, in recent years, data-based machine learning methods are disrupting physical layer research. Promising results have been presented, in particular, in the domain of deep learning (DL)-based channel estimation. In this paper, we present a performance and complexity analysis of packet detection and CFO estimation using both the conventional and the DL-based approaches. The goal of the study is to investigate under which conditions the performance of the DL-based methods approach or even surpass the conventional methods, but also, under which conditions their performance is inferior. Focusing on the emerging IEEE 802.11ah standard, our investigation uses both the standard-based simulated environment, and a real-world testbed based on Software Defined Radios.Comment: 13 pages, journal publicatio