Advancements of MultiRate Signal processing for Wireless Communication Networks: Current State Of the Art

With the hasty growth of internet contact and voice and information centric communications, many contact technologies have been urbanized to meet the stringent insist of high speed information transmission and viaduct the wide bandwidth gap among ever-increasing high-data-rate core system and bandwidth-hungry end-user complex. To make efficient consumption of the limited bandwidth of obtainable access routes and cope with the difficult channel environment, several standards have been projected for a variety of broadband access scheme over different access situation (twisted pairs, coaxial cables, optical fibers, and unchanging or mobile wireless admittance). These access situations may create dissimilar channel impairments and utter unique sets of signal dispensation algorithms and techniques to combat precise impairments. In the intended and implementation sphere of those systems, many research issues arise. In this paper we present advancements of multi-rate indication processing methodologies that are aggravated by this design trend. The thesis covers the contemporary confirmation of the current literature on intrusion suppression using multi-rate indication in wireless communiquE9; networks

Diffusion-Based Audio Inpainting

Audio inpainting aims to reconstruct missing segments in corrupted recordings. Previous methods produce plausible reconstructions when the gap length is shorter than about 100\;ms, but the quality decreases for longer gaps. This paper explores recent advancements in deep learning and, particularly, diffusion models, for the task of audio inpainting. The proposed method uses an unconditionally trained generative model, which can be conditioned in a zero-shot fashion for audio inpainting, offering high flexibility to regenerate gaps of arbitrary length. An improved deep neural network architecture based on the constant-Q transform, which allows the model to exploit pitch-equivariant symmetries in audio, is also presented. The performance of the proposed algorithm is evaluated through objective and subjective metrics for the task of reconstructing short to mid-sized gaps. The results of a formal listening test show that the proposed method delivers a comparable performance against state-of-the-art for short gaps, while retaining a good audio quality and outperforming the baselines for the longest gap lengths tested, 150\;ms and 200\;ms. This work helps improve the restoration of sound recordings having fairly long local disturbances or dropouts, which must be reconstructed.Comment: Submitted for publication to the Journal of Audio Engineering Society on January 30th, 202

Frame Theory for Signal Processing in Psychoacoustics

This review chapter aims to strengthen the link between frame theory and signal processing tasks in psychoacoustics. On the one side, the basic concepts of frame theory are presented and some proofs are provided to explain those concepts in some detail. The goal is to reveal to hearing scientists how this mathematical theory could be relevant for their research. In particular, we focus on frame theory in a filter bank approach, which is probably the most relevant view-point for audio signal processing. On the other side, basic psychoacoustic concepts are presented to stimulate mathematicians to apply their knowledge in this field

Model-based analysis of noisy musical recordings with application to audio restoration

This thesis proposes digital signal processing algorithms for noise reduction and enhancement of audio signals. Approximately half of the work concerns signal modeling techniques for suppression of localized disturbances in audio signals, such as impulsive noise and low-frequency pulses. In this regard, novel algorithms and modifications to previous propositions are introduced with the aim of achieving a better balance between computational complexity and qualitative performance, in comparison with other schemes presented in the literature. The main contributions related to this set of articles are: an efficient algorithm for suppression of low-frequency pulses in audio signals; a scheme for impulsive noise detection that uses frequency-warped linear prediction; and two methods for reconstruction of audio signals within long gaps of missing samples. The remaining part of the work discusses applications of sound source modeling (SSM) techniques to audio restoration. It comprises application examples, such as a method for bandwidth extension of guitar tones, and discusses the challenge of model calibration based on noisy recorded sources. Regarding this matter, a frequency-selective spectral analysis technique called frequency-zooming ARMA (FZ-ARMA) modeling is proposed as an effective way to estimate the frequency and decay time of resonance modes associated with the partials of a given tone, despite the presence of corrupting noise in the observable signal.reviewe

Time-frequency analysis based on split spectrum applied to audio and ultrasonic signals

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSignal processing is a large subject with applications integral to a number of technological fields such as communication, audio, Voice over IP (VoIP), pattern recognition, sonar, radar, ultrasound and medical imaging. Techniques exist for the analysis, modelling, extraction, recognition and synthesis of signals of interest. The focus of this thesis is signal processing for acoustics (both sonic and ultrasonic). In the applications examined, signals of interest are usually incomplete, distorted and/or noisy. Therefore, reconstructing the signal, noise reduction and removal of any distortion/interference are the main goals of the signal processing techniques presented. The primary aim is to study and develop an advanced time-frequency signal processing technique for acoustic applications to enhance the quality of the signals. In the first part of the thesis, a technique is presented that models and maintains the correlation between temporal and spectral parameters of audio signals. A novel Packet Loss Concealment (PLC) method is developed with applications to VoIP, audio broadcasting, and streaming. The problem of modelling the time-varying frequency spectrum in the context of PLC is addressed, and a novel solution is proposed for tracking and using the temporal motion of spectral flow to reconstruct the signal. The proposed method utilises a Time-Frequency Motion (TFM) matrix representation of the audio signal, where each frequency is tagged with a motion vector estimate that is assessed by cross-correlation of the movement of spectral energy within sub-bands across time frames. The missing packets are estimated using extrapolation or interpolation algorithms using a TFM matrix and then inverse transformed to the time-domain for reconstruction of the signal. The proposed method is compared with conventional approaches using objective Performance Evaluation of Speech Quality (PESQ), and subjective Mean Opinion Scores (MOS) in a range of packet loss from 5% to 20%. The evaluation results demonstrate that the proposed algorithm substantially improves performance by an average of 2.85% and 5.9% in terms of PESQ and MOS respectively. In the second part of the thesis, the proposed method is extended and modified to address challenges of excessive coherent noise arising from ultrasonic signals gathered during Guided Wave Testing (GWT). It is an advanced Non-destructive testing technique which is used over several branches of industry to inspect large structures for defects where the structural integrity is of concern. In such systems, signal interpretation can often be challenging due to the multi-modal and dispersive propagation of Ultrasonic Guided Waves (UGWs). The multi-modal and dispersive nature of the received signals hampers the ability to detect defects in a given structure. The Split-Spectrum Processing (SSP) method with application for such signal has been studied and reviewed quantitatively to measure the enhancement in terms of Signal-to-Noise Ratio (SNR) and spatial resolution. In this thesis, the influence of SSP filter bank parameters on these signals is studied and optimised to improve SNR and spatial resolution considerably. The proposed method is compared analytically and experimentally with conventional approaches. The proposed SSP algorithm substantially improves SNR by an average of 30dB. The conclusions reached in this thesis will contribute to the progression of the GWT technique through considerable improvement in defect detection capability.Centre for Electronic Systems Research (CESR) of Brunel University London, The National Structural Integrity Research Centre (NSIRC) and TWI Ltd

Development and applications of adaptive IIR and subband filters

Adaptive infinite impulse response (IIR) filter is a challenging research area. Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems. In this study, we consider IIR channel both for system identification and channel equalization purposes. We focus on four different approaches: Least Mean Square (LMS), Recursive Least Square (RLS), Genetic Algorithm (GA) and Subband Adaptive Filter (SAF). ). The performance of conventional LMS and RLS based IIR system identification and channel equalization are found with the help of computer simulations. And also the convergence speed and the ability to locate the global optimum solution using a population based algorithm named Genetic Algorithm is given

Real-time sound synthesis on a multi-processor platform

Real-time sound synthesis means that the calculation and output of each sound sample for a channel of audio information must be completed within a sample period. At a broadcasting standard, a sampling rate of 32,000 Hz, the maximum period available is 31.25 μsec. Such requirements demand a large amount of data processing power. An effective solution for this problem is a multi-processor platform; a parallel and distributed processing system. The suitability of the MIDI [Music Instrument Digital Interface] standard, published in 1983, as a controller for real-time applications is examined. Many musicians have expressed doubts on the decade old standard's ability for real-time performance. These have been investigated by measuring timing in various musical gestures, and by comparing these with the subjective characteristics of human perception. An implementation and its optimisation of real-time additive synthesis programs on a multi-transputer network are described. A prototype 81-polyphonic-note- organ configuration was implemented. By devising and deploying monitoring processes, the network's performance was measured and enhanced, leading to an efficient usage; the 88-note configuration. Since 88 simultaneous notes are rarely necessary in most performances, a scheduling program for dynamic note allocation was then introduced to achieve further efficiency gains. Considering calculation redundancies still further, a multi-sampling rate approach was applied as a further step to achieve an optimal performance. The theories underlining sound granulation, as a means of constructing complex sounds from grains, and the real-time implementation of this technique are outlined. The idea of sound granulation is quite similar to the quantum-wave theory, "acoustic quanta". Despite the conceptual simplicity, the signal processing requirements set tough demands, providing a challenge for this audio synthesis engine. Three issues arising from the results of the implementations above are discussed; the efficiency of the applications implemented, provisions for new processors and an optimal network architecture for sound synthesis

Robust periodic disturbance compensation via multirate control

Master'sMASTER OF ENGINEERIN