pwc-archive/papers-with-abstracts huggingface dataset:
Learning-based Robust Speaker Counting and Separation with the Aid of Spatial Coherence
Authors: Yicheng Hsu, Mingsian Bai
Date: 2023-03-13 00:00:00
Tasks: Speaker Separation, Speech Separation

A three-stage approach is proposed for speaker counting and speech separation in noisy and reverberant environments. In the spatial feature extraction, a spatial coherence matrix (SCM) is computed using whitened relative transfer functions (wRTFs) across time frames. The global activity functions of each speaker are estimated from a simplex constructed using the eigenvectors of the SCM, while the local coherence functions are computed from the coherence between the wRTFs of a time-frequency bin and the global activity function-weighted RTF of the target speaker. In speaker counting, we use the eigenvalues of the SCM and the maximum similarity of the interframe global activity distributions between two speakers as the input features to the speaker counting network (SCnet). In speaker separation, a global and local activity-driven network (GLADnet) is used to extract each independent speaker signal, which is particularly useful for highly overlapping speech signals. Experimental results obtained from the real meeting recordings show that the proposed system achieves superior speaker counting and speaker separation performance compared to previous publications without the prior knowledge of the array configurations.
Two-step Band-split Neural Network Approach for Full-band Residual Echo Suppression
Authors: Zihan Zhang, Shimin Zhang, Mingshuai Liu, Yanhong Leng, Zhe Han, Li Chen, Lei Xie
Date: 2023-03-13 00:00:00
Tasks: Acoustic echo cancellation

This paper describes a Two-step Band-split Neural Network (TBNN) approach for full-band acoustic echo cancellation. Specifically, after linear filtering, we split the full-band signal into wide-band (16KHz) and high-band (16-48KHz) for residual echo removal with lower modeling difficulty. The wide-band signal is processed by an updated gated convolutional recurrent network (GCRN) with U$^2$ encoder while the high-band signal is processed by a high-band post-filter net with lower complexity. Our approach submitted to ICASSP 2023 AEC Challenge has achieved an overall mean opinion score (MOS) of 4.344 and a word accuracy (WAcc) ratio of 0.795, leading to the 2$^{nd}$ (tied) in the ranking of the non-personalized track.
The NPU-Elevoc Personalized Speech Enhancement System for ICASSP2023 DNS Challenge
Authors: Xiaopeng Yan, Yindi Yang, Zhihao Guo, Liangliang Peng, Lei Xie
Date: 2023-03-13 00:00:00
Tasks: Speech Enhancement

This paper describes our NPU-Elevoc personalized speech enhancement system (NAPSE) for the 5th Deep Noise Suppression Challenge at ICASSP 2023. Based on the superior two-stage model TEA-PSE 2.0, our system particularly explores better strategy for speaker embedding fusion, optimizes the model training pipeline, and leverages adversarial training and multi-scale loss. According to the results, our system is tied for the 1st place in the headset track (track 1) and ranked 2nd in the speakerphone track (track 2).
ICASSP 2023 Speech Signal Improvement Challenge
Authors: Ross Cutler, Ando Saabas, Babak Naderi, Nicolae-Cătălin Ristea, Sebastian Braun, Solomiya Branets
Date: 2023-03-12 00:00:00

The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems. For example, in the ICASSP 2022 Deep Noise Suppression challenge, the improvement in the background and overall quality is impressive, but the improvement in the speech signal is not statistically significant. To improve the speech signal the following speech impairment areas must be addressed: coloration, discontinuity, loudness, reverberation, and noise. A training and test set was provided for the challenge, and the winners were determined using an extended crowdsourced implementation of ITU-T P.804's listening phase. The results show significant improvement was made across all measured dimensions of speech quality.
Relating EEG recordings to speech using envelope tracking and the speech-FFR
Authors: Mike Thornton, Danilo Mandic, Tobias Reichenbach
Date: 2023-03-11 00:00:00
Tasks: Decoder, EEG, Eeg Decoding, Electroencephalogram (EEG)

During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals. We report on an approach developed for the ICASSP 2023 'Auditory EEG Decoding' Signal Processing Grand Challenge. A simple ensembling method is shown to considerably improve upon the baseline decoder performance. Even higher classification rates are achieved by jointly decoding the speech-evoked frequency-following response and responses to the temporal envelope of speech, as well as by fine-tuning the decoders to individual subjects. Our results could have applications in the diagnosis of hearing disorders or in cognitively steered hearing aids.
Multi-Task Sub-Band Network For Deep Residual Echo Suppression
Authors: Jiayao Sun, Dawei Luo, Zhaoxia Li, Jindong Li, Yukai Ju, Yang Li
Date: 2023-03-11 00:00:00
Tasks: Action Detection, Activity Detection, Multi-Task Learning

This paper introduces the SWANT team entry to the ICASSP 2023 AEC Challenge. We submit a system that cascades a linear filter with a neural post-filter. Particularly, we adopt sub-band processing to handle full-band signals and shape the network with multi-task learning, where dual signal voice activity detection (DSVAD) and echo estimation are adopted as auxiliary tasks. Moreover, we particularly improve the time frequency convolution module (TFCM) to increase the receptive field using small convolution kernels. Finally, our system has ranked 4th in ICASSP 2023 AEC Challenge Non-personalized track.
The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge
Authors: Pengcheng Guo, He Wang, Bingshen Mu, Ao Zhang, Peikun Chen
Date: 2023-03-11 00:00:00
Tasks: Audio-Visual Speech Recognition, speech-recognition, Speech Recognition, Visual Speech Recognition

This paper describes our NPU-ASLP system for the Audio-Visual Diarization and Recognition (AVDR) task in the Multi-modal Information based Speech Processing (MISP) 2022 Challenge. Specifically, the weighted prediction error (WPE) and guided source separation (GSS) techniques are used to reduce reverberation and generate clean signals for each single speaker first. Then, we explore the effectiveness of Branchformer and E-Branchformer based ASR systems. To better make use of the visual modality, a cross-attention based multi-modal fusion module is proposed, which explicitly learns the contextual relationship between different modalities. Experiments show that our system achieves a concatenated minimum-permutation character error rate (cpCER) of 28.13\% and 31.21\% on the Dev and Eval set, and obtains second place in the challenge.
Rate-Distortion Optimization With Alternative References For UGC Video Compression
Authors: Xin Xiong, Eduardo Pavez, Antonio Ortega, Balu Adsumilli
Date: 2023-03-11 00:00:00
Tasks: Video Compression

User generated content (UGC) refers to videos that are uploaded by users and shared over the Internet. UGC may have low quality due to noise and previous compression. When re-encoding UGC for streaming or downloading, a traditional video coding pipeline will perform rate-distortion (RD) optimization to choose coding parameters. However, in the UGC video coding case, since the input is not pristine, quality ``saturation'' (or even degradation) can be observed, i.e., increased bitrate only leads to improved representation of coding artifacts and noise present in the UGC input. In this paper, we study the saturation problem in UGC compression, where the goal is to identify and avoid during encoding, the coding parameters and rates that lead to quality saturation. We proposed a geometric criterion for saturation detection that works with rate-distortion optimization, and only requires a few frames from the UGC video. In addition, we show how to combine the proposed saturation detection method with existing video coding systems that implement rate-distortion optimization for efficient compression of UGC videos.
Gradient-Descent Based Optimization of Constant Envelope OFDM Waveforms
Authors: David G. Felton, David A. Hague
Date: 2023-03-13 00:00:00
Tasks: Computational Efficiency

This paper describes a gradient-descent based optimization algorithm for synthesizing Constant Envelope Orthogonal Frequency Division Multiplexing (CE-OFDM) waveforms with low Auto-Correlation Function (ACF) sidelobes in a specified region of time-delays. The algorithm optimizes the Generalized Integrated Sidelobe Level (GISL) which controls the mainlobe and sidelobe structure of the waveform's ACF. The operations of this Gradient-Descent GISL (GD-GISL) algorithm are FFT-based making it computationally efficient. This computational efficiency facilitates the design of large dimensional waveform design problems. Simulations demonstrate the GD-GISL algorithm on CE-OFDM waveforms employing Phase-Shift Keying (PSK) symbols that take on a continuum of values (i.e, $M_{\text{PSK}} = \infty$). Results from these simulations show that the GD-GISL algorithm can indeed reduce ACF sidelobes in a desired region of time-delays. However, truncating the symbols to finite M-ary alphabets introduces perturbations to the waveform's instantaneous phase which increases the waveform's ACF sidelobe levels.
Bistatic CRLBs for TTD array resource allocation during JCAS
Authors: Ben Willetts
Date: 2023-03-13 00:00:00

This paper conveys the theoretical sensing capability of a Joint Communication and Sensing (JCAS) system that utilizes a True-Time-Delay (TTD) array configuration. The TTD beamformer separates subcarrier beams into different angular locations for wide-beam coverage. The bistatic sensing performance of a TTD array configuration is modeled as a function of the number of array elements and the fractional power supplied by the transmitter. The sensing capability when employing a TTD array configuration is derived using the Cram\'er-Rao Lower Bound (CRLB) of a bistatic delay-AOA(Angle Of Arrival)-phase log-likelihood function which is obtained by cross-correlating complex eigenvectors extracted from the quadratic time-frequency domain representation of multicomponent phase-coded signals. The multicomponent phase-codes used here are synchronization signals that are defined by the 5G communications standard. The bistatic delay-AOA-phase estimation capability whilst utilizing these signals and a TTD configuration was seen to be highly non-linear with respect to the number of elements and fractional power supplied for the surveillance scenario shown; an allocation of 30 antenna elements supplied with maximum power made a sub-meter and sub-degree estimation capability theoretically possible.
Graph Learning from Gaussian and Stationary Graph Signals
Authors: Andrei Buciulea, Antonio G. Marques
Date: 2023-03-13 00:00:00
Tasks: Graph Learning

Graphs have become pervasive tools to represent information and datasets with irregular support. However, in many cases, the underlying graph is either unavailable or naively obtained, calling for more advanced methods to its estimation. Indeed, graph topology inference methods that estimate the network structure from a set of signal observations have a long and well established history. By assuming that the observations are both Gaussian and stationary in the sought graph, this paper proposes a new scheme to learn the network from nodal observations. Consideration of graph stationarity overcomes some of the limitations of the classical Graphical Lasso algorithm, which is constrained to a more specific class of graphical models. On the other hand, Gaussianity allows us to regularize the estimation, requiring less samples than in existing graph stationarity-based approaches. While the resultant estimation (optimization) problem is more complex and non-convex, we design an alternating convex approach able to find a stationary solution. Numerical tests with synthetic and real data are presented, and the performance of our approach is compared with existing alternatives.
Location-based Physical Layer Authentication in Underwater Acoustic Communication Networks
Authors: Waqas Aman, Saif Al-Kuwari, Marwa Qaraqe
Date: 2023-03-12 00:00:00
Tasks: Position

Research in underwater communication is rapidly becoming attractive due to its various modern applications. An efficient mechanism to secure such communication is via physical layer security. In this paper, we propose a novel physical layer authentication (PLA) mechanism in underwater acoustic communication networks where we exploit the position/location of the transmitter nodes to achieve authentication. We perform transmitter position estimation from the received signals at reference nodes deployed at fixed positions in a predefined underwater region. We use time of arrival (ToA) estimation and derive the distribution of inherent uncertainty in the estimation. Next, we perform binary hypothesis testing on the estimated position to decide whether the transmitter node is legitimate or malicious. We then provide closed-form expressions of false alarm rate and missed detection rate resulted from binary hypothesis testing. We validate our proposal via simulation results, which demonstrate errors' behavior against the link quality, malicious node location, and receiver operating characteristic (ROC) curves. We also compare our results with the performance of previously proposed fingerprint mechanisms for PLA in underwater acoustic communication networks, for which we show a clear advantage of using the position as a fingerprint in PLA.
IFF: A Super-resolution Algorithm for Multiple Measurements
Authors: Zetao Fei, Hai Zhang
Date: 2023-03-12 00:00:00
Tasks: subspace methods, Super-Resolution

We consider the problem of reconstructing one-dimensional point sources from their Fourier measurements in a bounded interval $[-\Omega, \Omega]$. This problem is known to be challenging in the regime where the spacing of the sources is below the Rayleigh length $\frac{\pi}{\Omega}$. In this paper, we propose a super-resolution algorithm, called Iterative Focusing-localization and Filtering (IFF), to resolve closely spaced point sources from their multiple measurements that are obtained by using multiple unknown illumination patterns. The new proposed algorithm has a distinct feature in that it reconstructs the point sources one by one in an iterative manner and hence requires no prior information about the source numbers. The new feature also allows for a subsampling strategy that can circumvent the computation of singular-value decomposition for large matrices as in the usual subspace methods. A theoretical analysis of the methods behind the algorithm is also provided. The derived results imply a phase transition phenomenon in the reconstruction of source locations which is confirmed in numerical experiments. Numerical results show that the algorithm can achieve a stable reconstruction for point sources with a minimum separation distance that is close to the theoretical limit. The algorithm can be generalized to higher dimensions.
Towards bandwidth estimation for graph signal reconstruction
Authors: Ajinkya Jayawant, Antonio Ortega
Date: 2023-03-12 00:00:00

In numerous graph signal processing applications, data is often missing for a variety of reasons, and predicting the missing data is essential. In this paper, we consider data on graphs modeled as bandlimited graph signals. Predicting or reconstructing the unknown signal values for such a model requires an estimate of the signal bandwidth. In this paper, we address the problem of estimating the reconstruction errors, minimizing which would thereby provide an estimate of the signal bandwidth. In doing so, we design a cross-validation approach needed for stable graph signal reconstruction and propose a method for estimating the reconstruction errors for different choices of signal bandwidth. Using this technique, we are able to estimate the reconstruction error on a variety of real-world graphs.
Range-Doppler Spoofing in OFDM Signals for Preventing Wireless Passive Emitter Tracking
Authors: Antonios Argyriou
Date: 2023-03-12 00:00:00

Passive emitter tracking (PET) algorithms can estimate both the range and Doppler of a wireless emitter when it uses orthogonal frequency division multiplexing (OFDM). In this paper we are interested to prevent this from happening by an unauthorized receiver (URx). To accomplish that we introduce in the transmitted signal a \textit{spoofing signal} that varies across subcarriers and successive OFDM symbols. With this technique the emitter is not only able to spoof its actual range and Doppler (allowing covert communication in terms of these two parameters), but is also capable of producing additional false emitter signatures to further confuse the URx. To evaluate the performance of our approach we calculate the range-Doppler response at the URx for different system configurations of an 802.11-based system.
Domain-Knowledge-Aided Airborne Ground Moving Targets Tracking
Authors: Jianduo Chai, Shaoming He, Hyo-Sang Shin
Date: 2023-03-12 00:00:00

This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving targets tracking algorithm. To improve the accuracy of multiple targets tracking, the proposed algorithm incorporates domain knowledge into the joint probabilistic data association (JPDA) filter as state constraints. The domain knowledge considered in this paper includes both road information extracted from a given map and local traffic regulations. Conventional track update method is modified to enhance the capability of recognition of temporarily track loss. A variable structure multiple model (VS-MM) method is developed to assign the road segment to a given target. The effectiveness of proposed algorithm is demonstrated through extensive numerical simulations.
A Fourier Transform Approach for Automatic Detection of Oysters Spawning
Authors: Augustine Ukpebor, James C. Addy, Kamal Ali, Ali A. Humos
Date: 2023-03-12 00:00:00
Tasks: Time Series, Time Series Analysis

Various studies have been developed to monitor the gaping behavior of bivalves (oysters) in response to environmental factors. This work aims to fully automate oyster spawning detection in real-time by building on previous efforts. The sensor system developed at Jackson State University, Mississippi, employs the Hall effect phenomenon to accurately measure the gaping of bivalves accurately. The system uses a Hall effect sensor and a small magnet glued outside the bivalve shells. The Hall effect sensor reports the magnet distance, and hence the gape at a rate of 10 Hz, which the user may change. The data generated is stored on an SD card and supplied to a microcontroller for transmission to our file server on the Internet. The collected time series data is transformed to the frequency domain in 10-minute chunks using the fast Fourier transform (FFT) algorithm. When the data is analyzed in the frequency domain, the spectral power in the frequency range of 0.3 Hz to 1.3 Hz spikes several orders of magnitude when spawning occurs. It is then concluded that using a threshold of 0.1 dB in the frequency range of 0.3 Hz to 1.3 Hz, and spawning could be predicted 100% of the time.
Towards the Implementation of Smart Homes
Authors: Nesreen Mufid
Date: 2023-03-11 00:00:00

Automation technology has become an integral part of our daily lives, whether it's in our homes, cars, or workplaces. Thanks to the various communication interfaces and standards available today, implementing monitoring and automation systems has become a straightforward and efficient task.
Large-Scale Beam Placement and Resource Allocation Design for MEO-Constellation SATCOM
Authors: Vu Nguyen Ha, Eva Lagunas, Tedros Salih Abdu, Haythem Chaker, Symeon Chatzinotas, Joel Grotz
Date: 2023-03-11 00:00:00
Tasks: Clustering

This paper presents a centralized framework for optimizing the joint design of beam placement, power, and bandwidth allocation in an MEO satellite constellation to fulfill the heterogeneous traffic demands of a large number of global users. The problem is formulated as a mixed integer programming problem, which is computationally complex in large-scale systems. To overcome this challenge, a three-stage solution approach is proposed, including user clustering, cluster-based bandwidth and power estimation, and MEO-cluster matching. A greedy algorithm is also included as a benchmark for comparison. The results demonstrate the superiority of the proposed algorithm over the benchmark in terms of satisfying user demands and reducing power consumption.
Amino acid balancing for the prediction and evaluation of protein concentrations in cell-free protein synthesis systems
Authors: Jascha Rolf, Julian Handke, Frank Burzinski, Stephan Luetz, Katrin Rosenthal
Date: 2023-03-12 00:00:00

Cell-free protein synthesis (CFPS) systems are an attractive to complement the usual cell-based synthesis of proteins, especially for screening approaches. The literature describes a wide variety of CFPS systems, but their performance is difficult to compare since the reaction components are often used at different concentrations. Therefore, we have developed a calculation tool based on amino acid balancing to evaluate the performance of CFPS by determining the fractional yield as the ratio between theoretically achievable and achieved protein molar concentration. This tool was applied to a series of experiments from our lab and to various systems described in the literature to identify systems that synthesize proteins very efficiently and those that still have potential for higher yields. The well-established Escherichia coli system showed a high efficiency in the utilization of amino acids, but interestingly, less-attention-paid systems, such as the one based on Streptomyces lividans, also demonstrated exceptional fractional yields of 100%, meaning complete amino acid conversion. The methods and tools described here can quickly identify when a system has reached its maximum or has other limitations. We believe that the approach described here will facilitate the evaluation and optimization of existing CFPS systems and provide the basis for the systematic development of new CFPS systems.