The question of how harmonic sounds produce strong, low pitches at their fundamental frequencies, f0s, has been of theoretical and practical interest to scientists and engineers for many decades. Currently the best auditory models for f0pitch, e.g. [1], are based on bandpass filtering (cochlear mechanics), half-wave rectification and low-pass filtering (haircell transduction and synaptic transmission), channel autocorrelations (all-order interspike interval statistics) aggregated into a summary autocorrelation, and an analysis that determines the most prevalent interspike intervals. As a possible alternative to autocorrelation computations, we propose an alternative model that uses an adaptive Synchrony Capture Filterbank (SCFB) in which groups of filters or channels in a filterbank neighborhood are driven exclusively (captured) by dominant frequency components that are closest to them. The channel outputs are then adaptively phase aligned with respect to a common time reference to compute a Summary Phase Aligned Function (SPAF), aggregated across all channels, from which f0can be easily extracted.

We propose a novel algorithm for tracking multiple sinusoidal signals that is motivated by neural coding in the mammalian peripheral auditory system. A striking feature of auditory nerve activity is the phenomenon of “synchrony capture,” whereby the most intense frequency components in the stimulus dominate the temporal firing patterns of whole subpopulations of auditory nerve fibers (ANFs). A novel adaptive filterbank structure that emulates key aspects of synchrony capture is presented. The proposed filterbank has two components: a fixed bank of traditional gammatone (or equivalent) filters that are cascaded with a bank of adaptively-tunable bandpass filter triplets. The bandpass filters are tuned by using a voltage controlled oscillator (VCO) whose frequency is steered by a frequency discriminator loop (FDL). The resulting filterbank is used to process synthetic signals and speech. It is shown that the VCOs can track the low frequency harmonics in speech that evoke voice pitch at their fundamental (F0). For vowels, the VCOs faithfully track the strongest harmonic present in each formant region.

This work addresses the problem of identifying multiple fundamental frequencies in an acoustic signal. An auditory inspired peripheral signal processing model is proposed that functions in a manner more like a bank of FM receivers rather than a traditional filterbank. Such receivers lock on to a strong signal (synchrony capture, frequency capture) even in the presence of nearby only slightly weaker signal components. Once the individual signal components are resolved, the model subjects them to an instantaneous nonlinearity and then performs harmonic grouping by cross correlating the isolated components. After the harmonically-related components are grouped, their pitches are computed using a standard summary autocorrelation approach.

A new approach to representing a time-limited, and essentially bandpass signalx(t) , by a set of discrete frequency values is proposed. The set of discrete frequency values is the set of locations along the frequency axis at which (real and/or imaginary parts of) the Fourier transform of the signalx(t) cross certain levels (especially zero level). Analogously, invoking time-frequency duality, a set of time instants denoting the zero/level crossings of a waveformx(t) can be used to represent a bandlimited spectrumX(f) . The proposed signal representation is based on a simple bandpass signal model that exploits our prior knowledge of the bandwidth/timewidth of the signal. We call it a Sum-of-Sincs (SOS) model, where Sinc stands for the familiar sinx/xfunction. Given the discrete fequency/time locations, we can accurately reconstruct the signalx(t) or the spectrumX(f) by solving a simple eigenvalue or a least squares problem. Using this approach as the basis, we propose an analysis/synthesis algorithm to decompose and represent complex multicomponent signals like speech over the entire time-frequency region. The proposed signal representation is an alternative to standard analog to discrete conversion based on the sampling theorem, and in principle, possesses some of the desirable attributes of signal representation in natural sensory systems.

Using the human auditory system as a guide we propose a signal processing strategy for decomposing composite signals into bandpass signal components and extracting their features. We use two parallel filterbanks, one composed of a set of wideband overlapping filters and another consisting of a set of narrowband filters. The filterbanks cooperate to isolate regions of persistent and transient signal activity. The narrowband filters help identify spectral regions containing significant signal energy and groups of wideband filters in those regions are then optimally combined to isolate and track each of the bandpass signal components. Each group of wideband filters are combined to track one bandpass component while suppressing all other neighboring components. Narrowband filters in cascade with nonlinearities are used to characterize the transient components.

We present a new feature representation for speech recognition based on both amplitude modulation spectra (AMS) and frequency modulation spectra (FMS). A comprehensive modulation spectral (CMS) approach is defined and analyzed based on a modulation model of the band-pass signal. The speech signal is processed first by a bank of specially designed auditory band-pass filters. CMS are extracted from the output of the filters as the features for automatic speech recognition (ASR). A significant improvement is demonstrated in performance on noisy speech. On the Aurora 2 task the new features result in an improvement of 23.43% relative to traditional mel-cepstrum front-end features using a 3 GMM HMM back-end. Although the improvements are relatively modest, the novelty of the method and its potential for performance enhancement warrants serious attention for future-generation ASR applications.

We have developed a novel approach to speech feature extraction based on a modulation model of a band-pass signal. Speech is processed by a bank of band-pass filters. At the output of the band-pass filters the signal is subjected to a log-derivative operation which naturally decomposes the band-pass signal into analytic (called ? α(t)+j α) and anti-analytic (called ?β (t)-j β ) components. The average instantaneous frequency (AIF) and average log-envelope (ALE) are then extracted as coarse features at the output of each filter. Further, refined features may also be extracted from the analytic and anti-analytic components (but not done in this paper). We then evaluated the Aurora 2 task where noise corruption is synthetic. For clean training, (compared to the mel-cepstrum front end, with 3 mixture HMM back-end,) ourAIF/ALE front end achieves an average improvement of 13.97% with set A and 17.92% improvement with set B and -31.72% (negative) 'improvement' with set C. The overall improvement in accuracy rates for clean training is 7.97%. Although the improvements are modest, the novelty of the frontend and its potential for future enhancements are our strengths.

Line spectrum pairs (LSPs) are the roots (located in the complex-frequency or z-plane) of symmetric and antisymmetric polynomials synthesized using a linear prediction (LPC) polynomial. The angles of these roots, known as line-spectral frequencies (LSFs), implicitly represent the LPC polynomial and hence the spectral envelope of the underlying signal. By exploiting the duality between the time and frequency domains, we define analogous polynomials in the complex-time variable /spl zeta/. The angles of the roots of these polynomials in /spl zeta/-plane now correspond to zero-crossing time instants. Analogous to the fact that the line-spectral frequencies represent the spectral envelope of a signal, these zero-crossing locations can be used to represent the temporal envelope of bandpass signals.

The odd-order real zeros of a real-valued signal, s(t), correspond to its conventional or overt zero-crossings. The overt zero-crossings can be easily determined. But the complex zeros of s(t) are not easy to determine. This suggests the possibility of converting s(t) by using some invertible transformation, into a different signal, whose real zero-crossings alone determine s(t) completely. This process is known as real-zero conversion. The real zero-crossings of the transformed signal are termed covert zero-crossings (CoZeCs). Currently known real-zero conversion algorithms are impractical. In this paper we present a new adaptive algorithm for converting a band-pass signal into a pair of signals whose real zero-crossings essentially determine its envelope (up to a scale factor) and phase (up to a frequency translation).

We model a segment of filtered speech signal as a product of elementary signals as opposed to a sum of sinusoidal signals. Using this model, one can better appreciate the basic relationships between envelopes and phases or instantaneous frequencies (IFs) of signals. These relationships reveal some interesting properties of the signal's modulations. For instance, if the contribution due to a signal's envelope, specifically the Hilbert transform of its log-envelope, is removed from the signal's phase, then the resulting signal's IF is strictly positive. In addition, filtered speech signal having a bandwidth of B Hz can be essentially represented by the log-envelope and IF that have the same B Hz bandwidths. We extend the above ideas to decompose speech into modulated components. Specifically, a bank of data-adaptive filters (in a cross-coupled configuration) are used to decompose speech into its components; each adaptive filter is a simple single resonance bandpass filter (whose center-frequency or pole-location closely follows the desired formant frequency) supplemented by an adaptive all-zero filter (whose zero-locations sufficiently reduce unwanted leakage from neighboring formants). The filtered components are then represented by their respective log-envelopes and positive IFs; these small number of modulations closely approximate the speech signal.

We address the problem of estimating the envelope of a real valued signal, s(t), that is observed for a duration of T seconds. We model s(t) using a Fourier series, by considering periodic extensions of the signal. By using an analog of the autocorrelation method of linear prediction on the Fourier coefficients of s(t), the envelope of the signal is estimated without explicitly computing the analytic signal through Hilbert transformation. Using this method the envelope of a non-stationary signal can be computed by processing the signal through a sliding T-second window.

An analytic signal permits unambiguous characterization of the phase and envelope of a real signal. But the analytic signal's phase-derivative, i.e. the instantaneous frequency (IF) is typically a wild function and can take on values ranging from negative infinity to positive infinity. Fortunately, any analytic signal can be decomposed into a minimum phase (MinP) signal component and an all-phase (AllP) signal component. While the MinP signal's log-envelope and its phase form a Hilbert transform pair, the AllP signal has a positive definite instantaneous frequency (PIF) unlike that of the original analytic signal. We propose an elegant computational algorithm that separates the MinP and AllP components of the analytic signal. The envelope of the MinP component corresponds to the AM and the PIF of the AllP component corresponds to the positive FM.

Algorithms are presented for least-squares approximation of Toeplitz and Hankel matrices from noise corrupted or ill-composed matrices, which may not have correct structural or rank properties. Utilizing Caratheodery theorem on complex number representation to model the Toeplitz and Hankel matrices, it is shown that these matrices possess specific row and column structures. The inherent structures of the matrices are exploited to develop a computational algorithm for estimation of the matrices that are closest, in the Frobenius norm sense, to the given noisy or rank-excessive matrices. Simulation studies bear out the effectiveness of the proposed algorithms providing significantly better results than the state-space methods.

We address the problem of estimating the envelope of a real valued signal, s(t) that is observed for a duration of T seconds. We model s(t) using a Fourier series, by considering periodic extensions of the signal. By using an analog of the auto-correlation method of linear prediction on the Fourier coefficients of s(t) the envelope of the signal is estimated without explicitly computing the analytic signal.

A parametric approach to compute the Hilbert transform (HT) of a real signal is proposed. A scaled version of the real signal is raised to the exponent of Napier's base, and an all-pole signal model is fitted to approximate the resulting signal as though it were the envelope. An error criterion that flattens this envelope is then minimized over a set of complex Fourier coefficients. The resulting approximation is guaranteed to be a minimum-phase (MinP) signal. Since a MinP signal has the property that the logarithm of its envelope and its phase are related by the HT, it follows that the resulting phase of MinP approximation yields the desired HT.

This paper describes a new algorithm to identify voiced sections in a speech waveform and determine their pitch contours. The new pitch determination algorithm (PDA) uses variable frames and thus overcomes some common problems associated with fixed frame PDAs. It is adapted from an existing maximum likelihood PDA by retaining its strength and removing some weakness in the original formulation. The new PDA was tested on sixteen sentences from TIMIT database and its effectiveness is supported by the test results.

In an earlier paper, Rao and Kumaresan (see Proc. of the 29-th Asilomar Conf. on Sig., Sys., and Comp., 1995) proposed a new algorithm, called the parametric constant modulus algorithm (PCMA) to demodulate noiseless, synthetic as well as real-world, cochannel FM signals. They extend the PCMA to develop two new procedures. The first one addresses the problem of reserving two closely spaced noisy sinewaves (from a short data record). The second algorithm is applied to estimate the phase parameters of two superimposed noisy chirp signals; both algorithm's performance are compared with the Cramer-Rao bounds. The techniques described are of interest in wireless/military communications in the context of carrier frequency and Doppler estimation.

In this paper we examine the effect of model mismatch when modeling a signal consisting of multiple non-stationary sinusoids. The envelopes and frequencies of the components were modeled as polynomials of low order over short intervals of time and the coefficients estimated using the least-squares error criterion. If the block length and model orders are not properly chosen, unacceptable errors occurred in the estimated frequency tracks. The errors tended to increase as the number of components increased. Using the simpler constant envelope, constant frequency sinewave model for short, heavily overlapping blocks and smoothing the resulting frequency tracks gave surprisingly good results when analyzing a long duration multicomponent signal. We conclude that the more complex polynomial model may not always yield the expected increase in accuracy in signal modeling.

A procedure to decompose a signal consisting of nonstationary sinusoidal components based on the principles of residual signal analysis is proposed. A tracking unit consisting of an all zero filter (AZF) in cascade with a dynamic tracking filter (DTF) is assigned to each component. While the adaptively varying zeros of the AZF suppresses all interfering neighbors, the DTF captures the slowly varying instantaneous frequency (IF) of the desired component. Our earlier methods improved upon Costas's estimator-predictor filter bank by using a better interfering signal predictor. The alternative procedure described in this paper avoids the use of prediction and is not overly restricted by the number of components. We also show that by using two simple feedback loops (a loop-filter is thus avoided) the tracking information is ensured to be in phase. Finally, the algorithms ability to decompose synthetic signals, speech signals into harmonic partials as well as tracking the formants present in voiced speech segments is illustrated.

We describe an improved version of our previously proposed feed-back residual interfering signal canceler (RISC) algorithm that can now track the phases and amplitudes of the sinewave components present in a signal without any a priori knowledge. We also give the details of the feed-forward RISC algorithm for tracking the sinewave components present in voiced speech. The effectiveness of these algorithms is demonstrated by processing both synthetic and clean natural speech.>

The authors extend previous work on ambiguity resolution in sparse linear prediction to array signal processing. A non-uniformly spaced linear array with minimum number of sensors is introduced in order to obtain high resolution spatial frequency estimates at reduced computation and at minimum hardware cost. By properly choosing inter-element spacing, combining and processing subarray outputs in parallel, a computationally simple algorithm is proposed for unambiguously estimating spatial frequencies of the source signals.>

Techniques based on linear prediction (LP) and the singular value decomposition (SVD) for the robust estimation of the parameters of closely spaced exponentially damped sinusoidal signals in additive noise are extended and improved. An iterative method of fitting lower-rank least squares approximations subject to a general choice of weights is used. The method is applied to data sequences consisting of one and two signals with impulsive noise or with missing data samples.>

An adaptive finite impulse response (FIR) algorithm for adjusting the coefficients of an adaptive filter for suppressing periodic disturbances is presented. Due to the fact that the poles of the filter are spaced equidistantly, superior convergence properties and attenuations are obtained. The effectiveness of the adaptive filter is compared with the performance of known algorithms. The algorithm converges for ratios of fundamental frequency to sampling frequency which are much lower than those achievable with other algorithms and the attenuation of the filter to the periodic disturbances is much better.>

The authors propose a method to fit a discrete-time pole-zero filter model H(z)=A(z)/B(z) to arbitrary complex-valued frequency response samples Y(u/sub i/), i=0, 1, . . ., t in the least-square sense. An important attribute of the technique is that the frequency locations u/sub i/, i=0, 1, 2, . . ., t need not be equally spaced around the unit circle. The initial estimates of the denominator coefficients of the model system function H(z) are obtained by using an iterative algorithm. Starting from these initial estimates, a Newton-Raphson gradient technique is used to reach a minimum of the error criterion. The numerator polynomial coefficients are subsequently obtained by solving another set of linear equations. Simulation results using a filter design example are provided.>

Methods to estimate the parameters of a noisy signal are discussed. The signal typically consists of a large number of signal components. The signal components are assumed to be decaying exponentials. It is found that the standard iterative methods which attempt to fit a signal model are unsatisfactory. An iterative pole-by-pole method is proposed in which one signal component is updated in each iteration. This method is a promising method for system identification. It allows the incorporation of prior information which is useful in accelerating convergence, and produces reasonable results when the model order is high.>

A technique to extend the base of a residue number system (RNS) based on the Chinese remainder theorem (CRT) and the use of a redundant modulus, is proposed. The technique obtains the residue(s) of a given number in the extended moduli without resorting to the traditional mixed-radix conversion (MRC) algorithm. The base extension can be achieved in log/sub 2/n table lookup cycles, where n is the number of moduli in the RNS. The superiority of the technique, compared in terms of latency and hardware requirements to the traditional Szabo-Tanaka method is demonstrated.>

An improved structured matrix approximation approach for simultaneous estimation of frequencies and wavenumbers from 2-D array data is proposed. A quasi-linear relationship of the error with the polynomial coefficients of both the spatial and temporal domains is derived. This leads to an iterative optimization of the error criterion simultaneously in both the domains. By performing simulations it is shown that the method is capable of resolving signals closely spaced in frequency and wavenumber at low SNR. Next, the extendibility of the method for least-squares fitting of Toeplitz/Hankel/data matrix to a given non-Toeplitz/Hankel/data matrix is also discussed.>

Estimation of angles of arrival of plane waves from data observed at an array of sensors is performed with a network of interconnected, instantaneous, saturating non-linear elements called neurons. The networks use the observed data to decide which among a large number of hypothesized angles of arrivals best fits the data. A possible stochastic-digital implementation of such a network is also indicated.

A high resolution one-step algorithm for estimating the angles of arrivals of multiple wideband sources is proposed. For a dense and equally spaced array structure, a bilinear transformation is utilized in frequency domain for coherent signal subspace processing. When compared with existing coherent approaches, the proposed algorithm is noniterative and does not need knowledge of initial estimates of the arrival angles. The performance of the algorithm is presented using simulated data.

A fast and accurate magnitude scaling technique in the residue number system (RNS) is proposed. This technique obtains the residues of the scaled integer, when scaled by a product of a subset of the moduli, in approximately log n cycles, where n is the total number of moduli in the RNS. The scaled integer has an error of at most unity. The new technique is based on a judicious decomposition of the Chinese Remainder Theorem (CRT) and the use of a redundant channel which carries (at the least) the odd-even information about the integer being scaled.

A new multidimensional Hartley transform is defined and a vector-radix algorithm for fast computation of the transform is developed. The algorithm is shown to be faster (in terms of multiplication and addition count) compared to other related algorithms.

A new iterative technique for fitting a two-dimensional signal model to observed data in the least squares sense is proposed.

Memory intensive architectures are highly suited to VLSI technology. In this paper we present efficient architectures for implementing the discrete Fourier transform (DFT) and the discrete Hartley transform (DHART) based on prime factor decomposition. We use the multiplier-less distributed arithmetic in our architecture. But the look-up table sizes needed for implementing the short DFT/DHART modules are minimized by judiciously decomposing the short DFT/DHART matrices.

A new multidimensional Hartley Transform is defined and a vector-radix algorithm for fast computation of the transform is developed. The algorithm is shown to be faster (in terms of multiplication and addition count) compared to other related algorithms.

We consider the bearing estimation problem as a matrix approximation problem. The columns of a matrix X are embedded with the snapshot vectors from an N element array. The matrix X is approximated by a matrix XMin the least square sense. The rank, as well as the structure of the space spanned by columns of XM, are prespecified. After XMis computed, the bearings of the sources, the spatial correlation of the source signals can be estimated. Our technique is then compared with other methods such as MUSIC and SVD processing. When the number of snapshot vectors available for processing is large a simpler adaptive algorithm is suggested.

A prime factor FFT algorithm involving only real valued arithmetic is devised to compute the discrete Fourier transform of a real sequence. This letter extends an approach proposed by Bracewell.

This paper introduces a new concept of the VLSI-compatible multiprocessor system to perform most of digital signal processing functions, required in speech processing and modern telecommunications network. High performance is achieved by high degree of concurrency and parallelism in the architecture of the processor. Identified attributes of signal processing have influenced substantially the architecture. Set of macro instructions has been defined to be stored in the local memory inside the DSP chip. This approach reduces the access time and speeds-up overall operations. The key features of the DSP processor are presented. The multiprocessor architecture is described and possible configurations are discussed.

Estimates of frequencies of noise corrupted sinusoidal signals are often obtained from the zero locations of data adaptive or prediction error filters [1,2]. It is well known that the frequency estimation error decreases as the order of the prediction error filter is increased for a given data record length. In this paper, we explain this phenomenon, at high SNR by calculating the statistics of the estimation error.

Assuming that a short segment of observed speech signal can be modeled as the noisy impulse response of a linear system we obtain the pole locations of the system from the zeros of a polynomial. The coefficients of the polynomial are accurately determined by two methods using eigenvalue-eigenvector decomposition of an estimated correlation matrix. The speech-like signal is used in the reversed time direction to identify the signal related zeros of the Polynomial from the rest. Experimental results comparing the accuracy of the Pole parameters obtained by different methods are given.

We present more information about improved methods for estimation of the frequencies of sinusoids which are closely spaced in frequency and which are observed in the presence of noise using limited apertures in time and/or space. We compare our realization of maximum likelihood estimation of multiple frequencies with an improved version of Owsley's method and with the forward-backward prediction method advocated by Ulrych and Clayton and by Marple.