A DGPS/Radiobeacon Receiver for Minimum Shift Keying with Soft Decision Capabilities
The Global Positioning System (GPS) is now in operation, and many improvements to its performance are being sought. One such improvement is Differential GPS (DGPS), where known errors in the GPS broadcast are identified and the corrections broadcast to the end user. One implementation of DGPS being considered is the use of coastal marine radio direction finding (RDF) radiobeacons in the 285-325kHz band as transmitters for the DGPS broadcast. The normal RDF beacon signal consists of a continuous carrier on a one kilohertz boundary plus a Morse-code identification signal 1025Hz above the carrier. In the DGPS/radiobeacon implementation proposed for the US coastal regions, the differential data link signal uses minimum shift keying (MSK) at a data rate of 25, 50, 100, 200 or 400 baud (the exact baud rat has not yet been decided). This MSK signal is centered between the RDF beacon carrier and identification signal. At the frequencies that these radiobeacons are operated, the prevailing atmospheric noise is both non-Gaussian and very strong. This noise characteristic makes the design of a long-range data link difficult. One solution that has been proposed is the use of forward error correction (FEC) coding of the data. The performance of FEC decoders can be improved by the used of a soft decision receiver, which delivers both bit decisions and information about the validity of the bit decisions. This work describes the design of a radio receiver for DGPS/Radiobeacon servics which is capable of reception of 400 baud MSK in the DGPS/Radiobeacon band. The receiver is designed to be easily augmented to provide soft decisions and easily modified to recieve MSK at data rates of 25 to 400 baud. The radio is a microprocessor controlled dual conversion superheterodyne with an audio frequency of 1kHz. The demodulator runs on the same microprocessor that controls the radio. The weak-signal performance of the demodulator is very good: the Eb/No vs. bit error rate performance of the demodulator is only a couple of dB worse than the theoretical performance of differential phase-shift keying. The radio has a noise floor of -114dBm referenced to it's 500Hz wide audio bandwidth and a 3rd order intermodulation intercept of +7dBm for a dynamic range of 83dB. This work concludes with a thumbnail analysis of the operations needed to implement a soft bit decision estimator, and some suggestions for the implementation of said soft bit decision estimator.
EFFICIENT MAPPING OF ADVANCED SIGNAL PROCESSING ALGORITHMS ON MULTI-PROCESSOR ARCHITECTURES
Modern microprocessor technology is migrating from simply increasing clock speeds on a single processor to placing multiple processors on a die to increase throughput and power performance in every generation. To utilize the potential of such a system, signal processing algorithms have to be efficiently parallelized so that the load can be distributed evenly among the multiple processing units. In this paper, we study several advanced deterministic and stochastic signal processing algorithms and their computation using multiple processing units. Specifically, we consider two commonly used time-frequency signal representations, the short-time Fourier transform and the Wigner distribution, and we demonstrate their parallelization with low communication overhead. We also consider sequential Monte Carlo estimation techniques such as particle filtering, and we demonstrate that its multiple processor implementation requires large data exchanges and thus a high communication overhead. We propose a modified mapping scheme that reduces this overhead at the expense of a slight loss in accuracy, and we evaluate the performance of the scheme for a state estimation problem with respect to accuracy and scalability.
EngD thesis: Reduced-Complexity Signal Detection in Digital Communications Receivers
The Author began this Engineering Doctorate (EngD) in Autumn 1999, whilst already in full-time employment as a DSP software engineer at Nortel Networks, Harlow. This EngD comprises a set of three projects. The first project was focused on the development of dual-tone multi-frequency (DTMF) signal detection software. DTMF signals are currently used for operating menu-driven services such as voice-mail, telephone banking and share-dealing. The need for detection software in a packet networking environment exists because such signals become degraded when they travel through speech compression circuits. In addition, if they appear as echoes on a telephone line, they can affect the operation of echo cancellation systems. In this thesis a number of DSP algorithms are discussed where fast detection and minimum complexity are key characteristics. A key contribution here was the development of a novel detection algorithm based on the extraction of parameters that model the DTMF signal. The thesis reports a method combining parameter extraction with the technique of maximum likelihood to perform DTMF detection, resulting in very short time-frames when compared to standard approaches. Reducing the complexity of detection techniques is also important in today’s communication systems. To this end a key contribution here was the development of the ‘split Goertzel algorithm’, which permitted an overlapping of analysis windows without the need for reprocessing input data. Besides being applied to voice-band signals, such as in the case of DTMF, the Author also had the opportunity during the EngD to apply the skills and knowledge acquired in signal processing to higher-rate data-streams. This involved work concerning the equalisation of channel distortion commonly found in wireless communication systems. This covers two projects, with the first being conducted at Verticalband Ltd. (now no longer operational) in the area of digital television receivers. In this part of the thesis a real-time DSP implementation is discussed for enhancing a simulation system developed for wireless channels. A number of channel equalisation approaches are studied. The work concludes that for high-rate signals, non-linear algorithms have the best error rate performance. On the basis of the studies carried out, the thesis considers development and implementation issues of designs based on the decision feedback equaliser. The thesis reports on a software design which applies the method of least squares to carry out filter coefficient adaptation. The Verticalband studies reported lead on to further research within the context of channel equalisation, in the context of the detection of data in multiple-input multiple-output (MIMO) wireless local area network (WLAN) systems. This work was undertaken at Philips Research in Eindhoven, The Netherlands. The thesis discusses implementation scenarios of multi-element antenna arrays that aim to provide bit-rates orders of magnitude higher than today’s WLAN offerings, as those required by emerging standards such as 802.11n. The complexity of optimal detection techniques, such as maximum likelihood, scales exponentially with the number of transmit antennas. This translates to high processing costs and power consumption, rendering such techniques unsuitable for use in battery-powered devices. The initial main contribution here was the analysis of the complexity of algorithms whose performance had already been tested, such as the non-linear maximum likelihood approach and also less complex methods utilising linear filtering. This resulted in the development of new formulae to predict processing costs of algorithms based on the number of transmit and receive antennas. Other key contributions were defining a method to reduce the complexity of matrix inversion when using the Moore-Penrose pseudo-inverse, and the application of matrix decomposition to obviate the need for costly matrix inversion at all. Some on-going research into sub-optimal detection is also discussed, which describes methods to reduce the search-space for the maximum likelihood algorithm.
Ignal Enhancement Using Time-Frequency Based Denoising
This thesis investigates and compares time and wavelet-domain denoising techniques where received signals contain broadband noise. We consider how time and wavelet-domain denoising schemes and their combinations compare in the mean squared error sense. This work applies Wiener prediction and Median filtering as they do not require any prior signal knowledge. In the wavelet-domain we use soft or hard thresholding on the detail coefficients. In addition, we explore the effect of these wavelet-domain thresholding techniques on the coefficients associated with cycle-spinning and the newly proposed recursive cycle-spinning scheme. Finally, we note that thresholding does not make an attempt to de-noise coefficients that remain after thresholding; therefore we apply time domain techniques to the remaining detail coefficients from the first level of decomposition in an attempt to de-noise them further prior to reconstruction. This thesis applies and compares these techniques using a mean squared error criterion to identify the best performing in a robust test signal environment. We find that soft thresholding with Stein’s Unbiased Risk Estimate (SURE) thresholding produces the best mean squared error results in each test case and that the addition of Wiener prediction to the first level of decomposition coefficients leads to a slightly enhanced performance. Finally, we illustrate the effects of denoising algorithms on longer data segments.
Auditory Component Analysis Using Perceptual Pattern Recognition to Identify and Extract Independent Components From an Auditory Scene
The cocktail party effect, our ability to separate a sound source from a multitude of other sources, has been researched in detail over the past few decades, and many investigators have tried to model this on computers. Two of the major research areas currently being evaluated for the so-called sound source separation problem are Auditory Scene Analysis (Bregman 1990) and a class of statistical analysis techniques known as Independent Component Analysis (Hyvärinen 2001). This paper presents a methodology for combining these two techniques. It suggests a framework that first separates sounds by analyzing the incoming audio for patterns and synthesizing or filtering them accordingly, measures features of the resulting tracks, and finally separates sounds statistically by matching feature sets and making the output streams statistically independent. Artificial and acoustical mixes of sounds are used to evaluate the signal-to-noise ratio where the signal is the desired source and the noise is comprised of all other sources. The proposed system is found to successfully separate audio streams. The amount of separation is inversely proportional to the amount of reverberation present.
Adaptive Algorithms in Digital Signal Processing - Overview, Theory and Applications
OPTIMAL DESIGN OF DIGITAL EQUIVALENTS TO ANALOG FILTERS
The proposed optimal algorithm for the digitizing of analog filters is based on two existing filter design methods: the extended window design (EWD) and the matched–pole (MP) frequency sampling design. The latter is closely related to the filter design with iterative weighted least squares (WLS). The optimization is performed with an original MP design that yields an equiripple digitizing error. Then, a drastic reduction of the digitizing error is achieved through the introduction of a fractional time shift that minimizes the magnitude of the equiripple error within a given frequency interval. The optimal parameters thus obtained can be used to generate the EWD equations, together with a variable fractional delay output, as described in an earlier paper. Finally, in contrast to the WLS procedure, which relies on a “good guess” of the weighting function, the MP optimization is straightforward.
Digital Signal Processing Maths
Modern digital signal processing makes use of a variety of mathematical techniques. These techniques are used to design and understand efficient filters for data processing and control.
Wavelet Denoising for TDR Dynamic Range Improvement
A technique is presented for removing large amounts of noise present in time-domain-reflectometry (TDR) waveforms to increase the dynamic range of TDR waveforms and TDR based s-parameter measurements.
IMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISYIMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISY SIGNALS USING TMS320C6713 DSK
Periodogram Smoothing is a technique of power spectrum estimation. The discrete Fourier transform of a digital signal simply resolves the frequency components. The algorithm is implemented on Texas Instruments’ TMS320C6713 DSP Starter Kit (DSK). This is a 32-bit floating-point digital signal processor running at 225 MHz. The programs are basically written in the C programming language. However, those sections of code which are time-critical and memory-critical are written in assembly language of C6713. A MATLAB™ graphical user interface is also provided. The MATLAB™ program calls C programs loaded in Code Composer Studio (CCS). The C programs in turn call the assembly programs when required.
Auditory Component Analysis Using Perceptual Pattern Recognition to Identify and Extract Independent Components From an Auditory Scene
The cocktail party effect, our ability to separate a sound source from a multitude of other sources, has been researched in detail over the past few decades, and many investigators have tried to model this on computers. Two of the major research areas currently being evaluated for the so-called sound source separation problem are Auditory Scene Analysis (Bregman 1990) and a class of statistical analysis techniques known as Independent Component Analysis (Hyvärinen 2001). This paper presents a methodology for combining these two techniques. It suggests a framework that first separates sounds by analyzing the incoming audio for patterns and synthesizing or filtering them accordingly, measures features of the resulting tracks, and finally separates sounds statistically by matching feature sets and making the output streams statistically independent. Artificial and acoustical mixes of sounds are used to evaluate the signal-to-noise ratio where the signal is the desired source and the noise is comprised of all other sources. The proposed system is found to successfully separate audio streams. The amount of separation is inversely proportional to the amount of reverberation present.
Restoration of Nonlinearly Distorted Optical Soundtracks Using Regularized Inverse Characteristics
This dissertation is concerned with the possibilities of restoration of degraded film-sound. The sound-quality of old films are often not acceptable, which means that the sound is so noisy and distorted that the listener have to take strong efforts to understand the conversations in the film. In this case the film cannot give artistic enjoyment to the listener. This is the reason that several old films cannot be presented in movies or television. The quality of these films can be improved by digital restoration techniques. Since we do not have access to the original signal, only the distorted one, therefore we cannot adjust recording parameters or recording techniques. The only possibility is to post-compensate the signal to produce a better estimate about the undistorted, noiseless signal. In this dissertation new methods are proposed for fast and efficient restoration of nonlinear distortions in the optically recorded film soundtracks. First the nonlinear models and nonlinear restoration techniques are surveyed and the ill-posedness of nonlinear post-compensation (the extreme sensitivity to noise) is explained. The effects and sources of linear and nonlinear distortions at optical soundtracks are also described. A new method is proposed to overcome the ill-posedness of the restoration problem and to get an optimal result. The effectiveness of the algorithm is proven by simulations and restoration of real film-sound signals.
Through-Wall Imaging with UWB Radar System
Motivation: A man was interested in knowing of unknown from the very beginning of the human history. Our human eyes help us to investigate our environment by reflection of light. However, wavelengths of visible light allows transparent view through only a very small kinds of materials. On the other hand, Ultra WideBand (UWB) electromagnetic waves with frequencies of few Gigahertz are able to penetrate through almost all types of materials around us. With some sophisticated methods and a piece of luck we are able to investigate what is behind opaque walls. Rescue and security of the people is one of the most promising fields for such applications. Rescue: Imagine how useful can be information about interior of the barricaded building with terrorists and hostages inside for a policemen. The tactics of police raid can be build up on realtime information about ground plan of the room and positions of big objects inside. How useful for the firemen can be information about current interior state of the room before they get inside? Such hazardous environment, full of smoke with zero visibility, is very dangerous and each additional information can make the difference between life and death. Security: Investigating objects through plastic, rubber, dress or other nonmetallic materials could be highly useful as an additional tool to the existing x-ray scanners. Especially it could be used for scanning baggage at the airport, truckloads on borders, dangerous boxes, etc.
Fundamentals of the DFT (fft) Algorithms
In this article, a physical explanation of the fundamentals of the DFT (fft) algorithms is presented in terms of waveform decomposition. After reading the article and trying the examples, the reader is expected to gain a clear understanding of the basics of the mysterious DFT (fft) algorithms.
Audio Time-Scale Modification
Audio time-scale modification is an audio effect that alters the duration of an audio signal without affecting its perceived local pitch and timbral characteristics. There are two broad categories of time-scale modification algorithms, time-domain and frequency-domain. The computationally efficient time-domain techniques produce high quality results for single pitched signals such as speech, but do not cope well with more complex signals such as polyphonic music. The less efficient frequencydomain techniques have proven to be more robust and produce high quality results for a variety of signals; however they introduce a reverberant artefact into the output. This dissertation focuses on incorporating aspects of time-domain techniques into frequency-domain techniques in an attempt to reduce the presence of the reverberant artefact and improve upon computational demands. From a review of prior work it was found that there are a number of time-domain algorithms available and that the choice of algorithm parameters varies considerably in the literature. This finding prompted an investigation into the effects of the choice of parameters and a comparison of the various techniques employed in terms of computational requirements and output quality. The investigation resulted in the derivation of an efficient and flexible parameter set for use within time-domain implementations. Of the available frequency-domain approaches the phase vocoder and timedomain/ subband techniques offer an efficiency and robustness advantage over sinusoidal modelling and iterative phase update techniques, and as such were identified as suitable candidates for the provision of a framework for further investigation. Following from this observation, improvements in the quality produced by time-domain/subband techniques are realised through the use of a bark based subband partitioning approach and effective subband synchronisation techniques. In addition, computational and output quality improvements within a phase vocoder implementation are achieved by taking advantage of a certain level of flexibility in the choice of phase within such an implementation. The phase flexibility established is used to push or pull phase values into a phase coherent state. Further improvements are realised by incorporating features of time-domain algorithms into the system in order to provide a ‘good’ initial set of phase estimates; the transition to ‘perfect’ phase coherence is significantly reduced through this scheme, thereby improving the overall output quality produced. The result is a robust and efficient time-scale modification algorithm which draws upon various aspects of a number of general approaches to time-scale modification.
Acoustic Echo Cancellation using Digital Signal Processing
Acoustic echo cancellation is a common occurrence in todays telecommunication systems. It occurs when an audio source and sink operate in full duplex mode, an example of this is a hands-free loudspeaker telephone. In this situation the received signal is output through the telephone loudspeaker (audio source), this audio signal is then reverberated through the physical environment and picked up by the systems microphone (audio sink). The effect is the return to the distant user of time delayed and attenuated images of their original speech signal. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. This thesis focuses on the use of adaptive filtering techniques to reduce this unwanted echo, thus increasing communication quality. Adaptive filters are a class of filters that iteratively alter their parameters in order to minimise a function of the difference between a desired target output and their output. In the case of acoustic echo in telecommunications, the optimal output is an echoed signal that accurately emulates the unwanted echo signal. This is then used to negate the echo in the return signal. The better the adaptive filter emulates this echo, the more successful the cancellation will be. This thesis examines various techniques and algorithms of adaptive filtering, employing discrete signal processing in MATLAB. Also a real-time implementation of an adaptive echo cancellation system has been developed using the Texas Instruments TMS320C6711 DSP development kit.
Introduction to Digital Signal Processing
Nice slides introducing Digital Signal Processing.
A DGPS/Radiobeacon Receiver for Minimum Shift Keying with Soft Decision Capabilities
The Global Positioning System (GPS) is now in operation, and many improvements to its performance are being sought. One such improvement is Differential GPS (DGPS), where known errors in the GPS broadcast are identified and the corrections broadcast to the end user. One implementation of DGPS being considered is the use of coastal marine radio direction finding (RDF) radiobeacons in the 285-325kHz band as transmitters for the DGPS broadcast. The normal RDF beacon signal consists of a continuous carrier on a one kilohertz boundary plus a Morse-code identification signal 1025Hz above the carrier. In the DGPS/radiobeacon implementation proposed for the US coastal regions, the differential data link signal uses minimum shift keying (MSK) at a data rate of 25, 50, 100, 200 or 400 baud (the exact baud rat has not yet been decided). This MSK signal is centered between the RDF beacon carrier and identification signal. At the frequencies that these radiobeacons are operated, the prevailing atmospheric noise is both non-Gaussian and very strong. This noise characteristic makes the design of a long-range data link difficult. One solution that has been proposed is the use of forward error correction (FEC) coding of the data. The performance of FEC decoders can be improved by the used of a soft decision receiver, which delivers both bit decisions and information about the validity of the bit decisions. This work describes the design of a radio receiver for DGPS/Radiobeacon servics which is capable of reception of 400 baud MSK in the DGPS/Radiobeacon band. The receiver is designed to be easily augmented to provide soft decisions and easily modified to recieve MSK at data rates of 25 to 400 baud. The radio is a microprocessor controlled dual conversion superheterodyne with an audio frequency of 1kHz. The demodulator runs on the same microprocessor that controls the radio. The weak-signal performance of the demodulator is very good: the Eb/No vs. bit error rate performance of the demodulator is only a couple of dB worse than the theoretical performance of differential phase-shift keying. The radio has a noise floor of -114dBm referenced to it's 500Hz wide audio bandwidth and a 3rd order intermodulation intercept of +7dBm for a dynamic range of 83dB. This work concludes with a thumbnail analysis of the operations needed to implement a soft bit decision estimator, and some suggestions for the implementation of said soft bit decision estimator.
IMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISYIMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISY SIGNALS USING TMS320C6713 DSK
Periodogram Smoothing is a technique of power spectrum estimation. The discrete Fourier transform of a digital signal simply resolves the frequency components. The algorithm is implemented on Texas Instruments’ TMS320C6713 DSP Starter Kit (DSK). This is a 32-bit floating-point digital signal processor running at 225 MHz. The programs are basically written in the C programming language. However, those sections of code which are time-critical and memory-critical are written in assembly language of C6713. A MATLAB™ graphical user interface is also provided. The MATLAB™ program calls C programs loaded in Code Composer Studio (CCS). The C programs in turn call the assembly programs when required.
Hidden Markov Model based recognition of musical pattern in South Indian Classical Music
Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno musicological research and can become an indispensable tool for the search and comparison of music extracts within a large multimedia database. This paper finds an efficient method for recognizing isolated musical patterns in a monophonic environment, using Hidden Markov Model. Each pattern, to be recognized, is converted into a sequence of frequency jumps by means of a fundamental frequency tracking algorithm, followed by a quantizer. The resulting sequence of frequency jumps is presented to the input of the recognizer which use Hidden Markov Model. The main characteristic of Hidden Markov Model is that it utilizes the stochastic information from the musical frame to recognize the pattern. The methodology is tested in the context of South Indian Classical Music, which exhibits certain characteristics that make the classification task harder, when compared with Western musical tradition. Recognition of 100% has been obtained for the six typical music pattern used in practise. South Indian classical instrument, flute is used for the whole experiment.
A Subspace Based Approach to the Design, Implementation and Validation of Algorithms for Active Vibration Isolation Control
Vibration isolation endeavors to reduce the transmission of vibration energy from one structure (the source) to another (the receiver), to prevent undesirable phenomena such as sound radiation. A well-known method for achieving this is passive vibration isolation (PVI). In the case of PVI, mounts are used - consisting of springs and dampers - to connect the vibrating source to the receiver. The stiffness of the mount determines the fundamental resonance frequency of the mounted system and vibrations with a frequency higher than the fundamental resonance frequency are attenuated. Unfortunately, however, other design requirements (such as static stability) often impose a minimum allowable stiffness, thus limiting the achievable vibration isolation by passive means. A more promising method for vibration isolation is hybrid vibration isolation control. This entails that, in addition to PVI, an active vibration isolation control (AVIC) system is used with sensors, actuators and a control system that compensates for vibrations in the lower frequency range. Here, the use of a special form of AVIC using statically determinate stiff mounts is proposed. The mounts establish a statically determinate system of high stiffness connections in the actuated directions and of low stiffness connections in the unactuated directions. The latter ensures PVI in the unactuated directions. This approach is called statically determinate AVIC (SD-AVIC). The aim of the control system is to produce antidisturbance forces that counteract the disturbance forces stemming from the source. Using this approach, the vibration energy transfer from the source to the receiver is blocked in the mount due to the anti-forces. This thesis deals with the design of controllers generating the anti-forces by applying techniques that are commonly used in the field of signal processing. The control approaches - that are model-based - are both adaptive and fixed gain and feedforward and feedback oriented. The control approaches are validated using two experimental vibration isolation setups: a single reference single actuator single error sensor (SR-SISO) setup and a single reference input multiple actuator input multiple error sensor output (SR-MIMO) setup. Finding a plant model can be a problem. This is solved by using a black-box modelling strategy. The plants are identified using subspace model identification. It is shown that accurate linear models can be found in a straightforward manner by using small batches of recorded (sampled) time-domain data only. Based on the identified models, controllers are designed, implemented and validated. Due to resonance in mechanical structures, adaptive SD-AVIC systems are often hampered by slow convergence of the controller coefficients. In general, it is desirable that the SD-AVIC system yields fast optimum performance after it is switched on. To achieve this result and speed up the convergence of the adaptive controller coefficients, the so-called inverse outer factor model is included in the adaptive control scheme. The inner/outer factorization, that has to be performed to obtain the inverse outer factor model, is completely determined in state space to enable a numerically robust computation. The inverse outer factor model is also incorporated in the control scheme as a state space model. It is found that fast adaptation of the controller coefficients is possible. Controllers are designed, implemented and validated to suppress both narrowband and broadband disturbances. Scalar regularization is used to prevent actuator saturation and an unstable closed loop. In order to reduce the computational load of the controllers, several steps are taken including controller order reduction and implementation of lower order models. It is found that in all experiments the simulation and real-time results correspond closely for both the fixed gain and adaptive control situation. On the SR-SISO setup, reductions up to 5.0 dB are established in real-time for suppressing a broadband disturbance output (0-2 kHz) using feedback-control. On the SR-MIMO vibration isolation setup, using feedforward-control reductions of broadband disturbances (0-1 kHz) of 9.4 dB are established in real-time. Using feedback-control, reductions are established up to 3.5 dB in real-time (0-1 kHz). In case of the SR-MIMO setup, the values for the reduction are obtained by averaging the reductions obtained in all sensor outputs. The results pave the way for the next generation of algorithms for SD-AVIC.
Auditory System for a Mobile Robot
The auditory system of living creatures provides useful information about the world, such as the location and interpretation of sound sources. For humans, it means to be able to focus one's attention on events, such as a phone ringing, a vehicle honking, a person taking, etc. For those who do not suffer from hearing impairments, it is hard to imagine a day without being able to hear, especially in a very dynamic and unpredictable world. Mobile robots would also benefit greatly from having auditory capabilities. In this thesis, we propose an artificial auditory system that gives a robot the ability to locate and track sounds, as well as to separate simultaneous sound sources and recognising simultaneous speech. We demonstrate that it is possible to implement these capabilities using an array of microphones, without trying to imitate the human auditory system. The sound source localisation and tracking algorithm uses a steered beamformer to locate sources, which are then tracked using a multi-source particle filter. Separation of simultaneous sound sources is achieved using a variant of the Geometric Source Separation (GSS) algorithm, combined with a multisource post-filter that further reduces noise, interference and reverberation. Speech recognition is performed on separated sources, either directly or by using Missing Feature Theory (MFT) to estimate the reliability of the speech features. The results obtained show that it is possible to track up to four simultaneous sound sources, even in noisy and reverberant environments. Real-time control of the robot following a sound source is also demonstrated. The sound source separation approach we propose is able to achieve a 13.7 dB improvement in signal-to-noise ratio compared to a single microphone when three speakers are present. In these conditions, the system demonstrates more than 80% accuracy on digit recognition, higher than most human listeners could obtain in our small case study when recognising only one of these sources. All these new capabilities will allow humans to interact more naturally with a mobile robot in real life settings.
Least Squares and Adaptive Multirate Filtering
This thesis addresses the problem of estimating a random process from two observed signals sampled at different rates. The case where the low–rate observation has a higher signal–to– noise ratio than the high–rate observation is addressed. Both adaptive and non–adaptive filtering techniques are explored. For the non–adaptive case, a multirate version of the Wiener–Hopf optimal filter is used for estimation. Three forms of the filter are described. It is shown that using both observations with this filter achieves a lower mean–squared error than using either sequence alone. Furthermore, the amount of training data to solve for the filter weights is comparable to that needed when using either sequence alone. For the adaptive case, a multirate version of the LMS adaptive algorithm is developed. Both narrowband and broadband interference are removed using the algorithm in an adaptive noise cancellation scheme. The ability to remove interference at the high rate using observations taken at the low rate without the high–rate observations is demonstrated.
Voice Activity Detection. Fundamentals and Speech Recognition System Robustness
An important drawback affecting most of the speech processing systems is the environmental noise and its harmful effect on the system performance. Examples of such systems are the new wireless communications voice services or digital hearing aid devices. In speech recognition, there are still technical barriers inhibiting such systems from meeting the demands of modern applications. Numerous noise reduction techniques have been developed to palliate the effect of the noise on the system performance and often require an estimate of the noise statistics obtained by means of a precise voice activity detector (VAD). Speech/non-speech detection is an unsolved problem in speech processing and affects numerous applications including robust speech recognition, discontinuous transmission, real-time speech transmission on the Internet or combined noise reduction and echo cancellation schemes in the context of telephony. The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases. During the last decade, numerous researchers have developed different strategies for detecting speech on a noisy signal and have evaluated the influence of the VAD effectiveness on the performance of speech processing systems. Most of the approaches have focussed on the development of robust algorithms with special attention being paid to the derivation and study of noise robust features and decision rules. The different VAD methods include those based on energy thresholds, pitch detection, spectrum analysis, zero-crossing rate, periodicity measure, higher order statistics in the LPC residual domain or combinations of different features. This chapter shows a comprehensive approximation to the main challenges in voice activity detection, the different solutions that have been reported in a complete review of the state of the art and the evaluation frameworks that are normally used. The application of VADs for speech coding, speech enhancement and robust speech recognition systems is shown and discussed. Three different VAD methods are described and compared to standardized and recently reported strategies by assessing the speech/non-speech discrimination accuracy and the robustness of speech recognition systems.
Audio Time-Scale Modification
Audio time-scale modification is an audio effect that alters the duration of an audio signal without affecting its perceived local pitch and timbral characteristics. There are two broad categories of time-scale modification algorithms, time-domain and frequency-domain. The computationally efficient time-domain techniques produce high quality results for single pitched signals such as speech, but do not cope well with more complex signals such as polyphonic music. The less efficient frequencydomain techniques have proven to be more robust and produce high quality results for a variety of signals; however they introduce a reverberant artefact into the output. This dissertation focuses on incorporating aspects of time-domain techniques into frequency-domain techniques in an attempt to reduce the presence of the reverberant artefact and improve upon computational demands. From a review of prior work it was found that there are a number of time-domain algorithms available and that the choice of algorithm parameters varies considerably in the literature. This finding prompted an investigation into the effects of the choice of parameters and a comparison of the various techniques employed in terms of computational requirements and output quality. The investigation resulted in the derivation of an efficient and flexible parameter set for use within time-domain implementations. Of the available frequency-domain approaches the phase vocoder and timedomain/ subband techniques offer an efficiency and robustness advantage over sinusoidal modelling and iterative phase update techniques, and as such were identified as suitable candidates for the provision of a framework for further investigation. Following from this observation, improvements in the quality produced by time-domain/subband techniques are realised through the use of a bark based subband partitioning approach and effective subband synchronisation techniques. In addition, computational and output quality improvements within a phase vocoder implementation are achieved by taking advantage of a certain level of flexibility in the choice of phase within such an implementation. The phase flexibility established is used to push or pull phase values into a phase coherent state. Further improvements are realised by incorporating features of time-domain algorithms into the system in order to provide a ‘good’ initial set of phase estimates; the transition to ‘perfect’ phase coherence is significantly reduced through this scheme, thereby improving the overall output quality produced. The result is a robust and efficient time-scale modification algorithm which draws upon various aspects of a number of general approaches to time-scale modification.
A Nonlinear Stein Based Estimator for Multichannel Image Denoising
The use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions. However, the observed images are often corrupted by an additive Gaussian noise. In this paper, we are interested in multichannel image denoising based on a multiscale representation of the images. A multivariate statistical approach is adopted to take into account both the spatial and the inter-component correlations existing between the different wavelet subbands. More precisely, we propose a new parametric nonlinear estimator which generalizes many reported denoising methods. The derivation of the optimal parameters is achieved by applying Stein’s principle in the multivariate case. Experiments performed on multispectral remote sensing images clearly indicate that our method outperforms conventional wavelet denoising techniques.
Specifying the Maximum Amplifier Noise When Driving an ADC
I recently learned an interesting rule of thumb regarding the use of an amplifier to drive the input of an analog to digital converter (ADC). The rule of thumb describes how to specify the maximum allowable noise power of the amplifier.
Hidden Markov Model based recognition of musical pattern in South Indian Classical Music
Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno musicological research and can become an indispensable tool for the search and comparison of music extracts within a large multimedia database. This paper finds an efficient method for recognizing isolated musical patterns in a monophonic environment, using Hidden Markov Model. Each pattern, to be recognized, is converted into a sequence of frequency jumps by means of a fundamental frequency tracking algorithm, followed by a quantizer. The resulting sequence of frequency jumps is presented to the input of the recognizer which use Hidden Markov Model. The main characteristic of Hidden Markov Model is that it utilizes the stochastic information from the musical frame to recognize the pattern. The methodology is tested in the context of South Indian Classical Music, which exhibits certain characteristics that make the classification task harder, when compared with Western musical tradition. Recognition of 100% has been obtained for the six typical music pattern used in practise. South Indian classical instrument, flute is used for the whole experiment.
Blind Adaptive Dereverberation of Speech Signals Using a Microphone Array
In this thesis, we present a blind adaptive speech dereverberation method based on the use of a reduced mutually referenced equalizers (RMRE) criterion. The method is based on the idea of the inversion of single-input multiple-output FIR linear systems, and as such requires the use of multiple microphones. However, unlike many traditional microphone array methods, there is no need for a specific array configuration or geometry. The RMRE method finds a subset of equalizers for a given delay in a single step, without the need for the typical channel estimation step. This makes the method practical in terms of implementation and avoids the pitfalls of the more complicated two step dereverberation approach, typical in many inversion methods. Additionally, only the second-order statistics of the signals recorded by the microphones are used, without the need for utilizing higher-order statistics information typically needed when the channsls have a nonminimum phase response, as is the case with room impulse responses. We present simulations and experimental results that demonstrate the applicability of the method when the input is speech, and show that in the noiseless case, perfect dereverberation can be achieved. We also evaluate its performance in the presence of noise, and we present a possible way to modify the proposed RMRE to work for very low SNR values. We also explore the problems when model-order mismatches are present, and demonstrate that the under-modeling of the channel impulse responses order can be combated by increasing the number of microphones. For order over-estimation, we will show that RMRE can handle such errors with no modification.
Code Acquisition using Smart Antennas with Adaptive Filtering Scheme for DS-CDMA Systems
Pseudo-noise (PN) code synchronizer is an essential element of direct-sequence code division multiple access (DS-CDMA) system because data transmission is possible only after the receiver accurately synchronizes the locally generated PN code with the incoming PN code. The code synchronization is processed in two steps, acquisition and tracking, to estimate the delay offset between the two codes. Recently, the adaptive LMS filtering scheme has been proposed for performing both code acquisition and tracking with the identical structure, where the LMS algorithm is used to adjust the FIR filter taps to search for the value of delay-offset adaptively. A decision device is employed in the adaptive LMS filtering scheme as a decision variable to indicate code synchronization, hence it plays an important role for the performance of mean acquisition time (MAT). In this thesis, only code acquisition is considered. In this thesis, a new decision device, referred to as the weight vector square norm (WVSN) test method, is devised associated with the adaptive LMS filtering scheme for code acquisition in DS-CDMA system. The system probabilities of the proposed scheme are derived for evaluating MAT. Numerical analyses and simulation results verify that the performance of the proposed scheme, in terms of detection probability and MAT, is superior to the conventional scheme with mean-squared error (MSE) test method, especially when the signal-to-interference-plus-noise ratio (SINR) is relatively low. Furthermore, an efficient and joint-adaptation code acquisition scheme, i.e., a smart antenna coupled with the proposed adaptive LMS filtering scheme with the WVSN test method, is devised for applying to a base station, where all antenna elements are employed during PN code acquisition. This new scheme is a process of PN code acquisition and the weight coefficients of smart antenna jointly and adaptively. Numerical analyses and simulation results demonstrate that the performance of the proposed scheme with five antenna elements, in terms of the output SINR, the detection probability and the MAT, can be improved by around 7 dB, compared to the one with single antenna case.
