Noise reduction in speech signals using a TMS320C31 digital signal processor

This paper describes a speech enhancement system (SES) based on a TMS320C31 digital signal processor (DSP) for real-time application. The SES algorithm is based on a modified spectral subtraction method and a new speech activity detector (SAD) is used. The system presents a medium computational load and a sampling rate up to 18 kHz can be used. The goal is load and a sampling rate up to 18 kHz can be used. The goal is to use it to reduce noise in an analog telephone line.

Wow! Linear systems and signal processing is fun!

We describe a recent offering of a linear systems and signal processing course for third-year electrical and computer engineering students. This course is a pre-requisite for our first digital signal processing course. Students have traditionally viewed linear systems courses as mathematical and extremely difficult. Without compromising the rigor of the required concepts, we strived to make the course fun, with application-based hands-on laboratory projects. These projects can be modified easily to meet specific instructors' preferences. © 2011 IEEE.(17 refs)

Is complex signal processing for bone conduction hearing aids useful?

OBJECTIVES To establish whether complex signal processing is beneficial for users of bone anchored hearing aids. METHODS Review and analysis of two studies from our own group, each comparing a speech processor with basic digital signal processing (either Baha Divino or Baha Intenso) and a processor with complex digital signal processing (either Baha BP100 or Baha BP110 power). The main differences between basic and complex signal processing are the number of audiologist accessible frequency channels and the availability and complexity of the directional multi-microphone noise reduction and loudness compression systems. RESULTS Both studies show a small, statistically non-significant improvement of speech understanding in quiet with the complex digital signal processing. The average improvement for speech in noise is +0.9 dB, if speech and noise are emitted both from the front of the listener. If noise is emitted from the rear and speech from the front of the listener, the advantage of the devices with complex digital signal processing as opposed to those with basic signal processing increases, on average, to +3.2 dB (range +2.3 … +5.1 dB, p ≤ 0.0032). DISCUSSION Complex digital signal processing does indeed improve speech understanding, especially in noise coming from the rear. This finding has been supported by another study, which has been published recently by a different research group. CONCLUSIONS When compared to basic digital signal processing, complex digital signal processing can increase speech understanding of users of bone anchored hearing aids. The benefit is most significant for speech understanding in noise.

Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

Signal processing in a loudspeaking telephone

One of the major problems associated with communication via a loudspeaking telephone (LST) is that, using analogue processing, duplex transmission is limited to low-loss lines and produces a low acoustic output. An architectural for an instrument has been developed and tested, which uses digital signal processing to provide duplex transmission between a LST and a telopnone handset over most of the B.T. network. Digital adaptive-filters are used in the duplex LST to cancel coupling between the loudspeaker and microphone, and across the transmit to receive paths of the 2-to-4-wire converter. Normal movement of a person in the acoustic path causes a loss of stability by increasing the level of coupling from the loudspeaker to the microphone, since there is a lag associated the adaptive filters learning about a non-stationary path, Control of the loop stability and the level of sidetone heard by the hadset user is by a microprocessoe, which continually monitors the system and regulates the gain. The result is a system which offers the best compromise available based on a set of measured parameters.A theory has been developed which gives the loop stability requirements based on the error between the parameters of the filter and those of the unknown path. The programme to develope a low-cost adaptive filter in LST produced a low-cost adaptive filter in LST produced a unique architecture which has a number of features not available in any similar system. These include automatic compensation for the rate of adaptation over a 36 dB range of output level, , 4 rates of adaptation (with a maximum of 465 dB/s), plus the ability to cascade up to 4 filters without loss o performance. A complex story has been developed to determine the adptation which can be achieved using finite-precision arithmatic. This enabled the development of an architecture which distributed the normalisation required to achieve optimum rate of adaptation over the useful input range. Comparison of theory and measurement for the adaptive filter show very close agreement. A single experimental LST was built and tested on connections to hanset telephones over the BT network. The LST demonstrated that duplex transmission was feasible using signal processing and produced a more comfortable means of communication beween people than methods emplying deep voice-switching to regulate the local-loop gain. Although, with the current level of processing power, it is not a panacea and attention must be directed toward the physical acoustic isolation between loudspeaker and microphone.

Signal processing for molecular and cellular biological physics:an emerging field

Recent advances in our ability to watch the molecular and cellular processes of life in action-such as atomic force microscopy, optical tweezers and Forster fluorescence resonance energy transfer-raise challenges for digital signal processing (DSP) of the resulting experimental data. This article explores the unique properties of such biophysical time series that set them apart from other signals, such as the prevalence of abrupt jumps and steps, multi-modal distributions and autocorrelated noise. It exposes the problems with classical linear DSP algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists. It is argued that these new methods applied in this context typify the nascent field of biophysical DSP. Practical experimental examples are supplied.

Statistical reference criteria for adaptive signal processing in digital communications

A general criterion for the design of adaptive systemsin digital communications called the statistical reference criterionis proposed. The criterion is based on imposition of the probabilitydensity function of the signal of interest at the outputof the adaptive system, with its application to the scenario ofhighly powerful interferers being the main focus of this paper.The knowledge of the pdf of the wanted signal is used as adiscriminator between signals so that interferers with differingdistributions are rejected by the algorithm. Its performance isstudied over a range of scenarios. Equations for gradient-basedcoefficient updates are derived, and the relationship with otherexisting algorithms like the minimum variance and the Wienercriterion are examined.

ECG denoising based on adaptive signal processing technique

An Electrocardiogram (ECG) monitoring system deals with several challenges related with noise sources. The main goal of this text was the study of Adaptive Signal Processing Algorithms for ECG noise reduction when applied to real signals. This document presents an adaptive ltering technique based on Least Mean Square (LMS) algorithm to remove the artefacts caused by electromyography (EMG) and power line noise into ECG signal. For this experiments it was used real noise signals, mainly to observe the di erence between real noise and simulated noise sources. It was obtained very good results due to the ability of noise removing that can be reached with this technique. A recolha de sinais electrocardiogr a cos (ECG) sofre de diversos problemas relacionados com ru dos. O objectivo deste trabalho foi o estudo de algoritmos adaptativos para processamento digital de sinal, para redu c~ao de ru do em sinais ECG reais. Este texto apresenta uma t ecnica de redu c~ao de ru do baseada no algoritmo Least Mean Square (LMS) para remo c~ao de ru dos causados quer pela actividade muscular (EMG) quer por ru dos causados pela rede de energia el ectrica. Para as experiencias foram utilizados ru dos reais, principalmente para aferir a diferen ca de performance do algoritmo entre os sinais reais e os simulados. Foram conseguidos bons resultados, essencialmente devido as excelentes caracter sticas que esta t ecnica tem para remover ru dos.

" Nonlinear Signal Processing of Electroencephalogram .' Application in the Study of Neurodynamics. "

Interfacings of various subjects generate new field ofstudy and research that help in advancing human knowledge. One of the latest of such fields is Neurotechnology, which is an effective amalgamation of neuroscience, physics, biomedical engineering and computational methods. Neurotechnology provides a platform to interact physicist; neurologist and engineers to break methodology and terminology related barriers. Advancements in Computational capability, wider scope of applications in nonlinear dynamics and chaos in complex systems enhanced study of neurodynamics. However there is a need for an effective dialogue among physicists, neurologists and engineers. Application of computer based technology in the field of medicine through signal and image processing, creation of clinical databases for helping clinicians etc are widely acknowledged. Such synergic effects between widely separated disciplines may help in enhancing the effectiveness of existing diagnostic methods. One of the recent methods in this direction is analysis of electroencephalogram with the help of methods in nonlinear dynamics. This thesis is an effort to understand the functional aspects of human brain by studying electroencephalogram. The algorithms and other related methods developed in the present work can be interfaced with a digital EEG machine to unfold the information hidden in the signal. Ultimately this can be used as a diagnostic tool.

Compensatory signal processing for profoundly hearing-impaired listeners: the transmission of fundamental frequency

This paper reviews a study to determine the usefulness of signal processing along with lipreading in improving speech perception of profoundly hearing impaired persons.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

Dressing monitoring with a digital signal processor

Grinding is a finishing process in machining operations, and the topology of the grinding tool is responsible for producing the desired result on the surface of the machined material The tool topology is modeled in the dressing process and precision is therefore extremely important This study presents a solution in the monitoring of the dressing process, using a digital signal processor (DSP) operating in real time to detect the optimal dressing moment To confirm the monitoring efficiency by DSP, the results were compared with those of a data acquisition system (DAQ) and offline processing The method employed here consisted of analyzing the acoustic emission and electrical power signal by applying the DPO and DPKS parameters The analysis of the results allowed us to conclude that the application of the DPO and DPKS parameters can be substituted by processing of the mean acoustic emission signal, thus reducing the computational effort

Photonics Crystals: The Future of Microwave Signal Processing

The ability of cryogenic photonic crystals to carry out high performance microwave signal processing operations has been developed into systems that can: rapidly record broadband microwave spectra with fine resolution and high dynamic range; search for patterns in 40 gigabits per second data streams; and communicate via spread- spectrum signals that are well below the noise floor. The basic concepts of the technology and its many applications, along with an overview of university-industry partnerships and the growing photonics industry in Bozeman, will be presented.