Prototype Waveform Research Articles

Automatic identification of slow biphasic complexes in EEG: an effective tool to detect encephalitis

Objective: the presence of slow biphasic complexes in EEG was found to be associated to different neural diseases, in particular inflammatory. This paper proposes a method to identify automatically the complexes and provides preliminary tests on paediatric patients with encephalitis. Approach: a prototype waveform was computed aligning and averaging complexes manually identified during years of investigation. A wide range of amplitudes and durations was noticed. The proposed automatic method is based on the cross-correlation of the tested EEG with scaled versions of the prototype waveform. The waves with high correlation with the prototype are identified as complexes if their amplitude and duration are reasonable and if they do not appear as pseudo-periodic oscillations. Main results: the algorithm was tested on a dataset of 128 EEGs from healthy controls and patients (for which the follow-up was also available). Experts assigned to each trace a severity score with 5 levels considering both electrophysiological and clinical manifestations. The number and amplitude of complexes are markers of encephalitis, they show statistically significant differences across EEGs with different severity scores and correlate with the condition of patients during the follow-up (median correlation about 80%). Significance: inflammatory brain pathologies can have serious sequelae. Quickness of diagnosis and targeted therapeutic interventions are essential to avoid or limit the damages. The proposed automated method is feasible for the fast, accurate and non-invasive diagnosis of encephalitis and the follow-up of patients.

Learning Recurrent Waveforms Within EEGs.

We demonstrate an algorithm to automatically learn the time-limited waveforms associated with phasic events that repeatedly appear throughout an electroencephalogram. To learn the phasic event waveforms we propose a multiscale modeling process that is based on existing shift-invariant dictionary learning algorithms. For each channel, waveforms at different temporal scales are learned based on the assumption that only a few waveforms occur in any window of the time-series, but the same waveforms reoccur throughout the signal. Once the waveforms are learned, the timing and amplitude of the phasic event occurrences are estimated using matching pursuit. To summarize the waveforms learned across multiple channels and subjects, we analyze their frequency content, their similarity to Gabor-Morlet wavelets, and perform shift-invariant k-means to cluster the waveforms. A prototype waveform from each cluster is then tested for differential spatial patterns between different motor imagery conditions. On multiple human EEG datasets, the learned waveforms capture key characteristics of signals they were trained to represent, with a consistency in waveform morphology and frequency content across multiple training sections and initializations. On multichannel datasets, the spatial amplitude patterns of the waveforms are also consistent and can be used to distinguish different modalities of motor imagery. We explored a methodology that can be used for modeling the recurrent waveforms in EEG traces. The methodology automatically identifies the most frequent phasic event waveforms in EEG, which could then be used as features for automatic evaluation and comparison of EEG during sleep, pathology, or mentally engaging tasks.

Low bit-rate voice compression based on frequency domain interpolative techniques

This paper presents an approach, referred to as frequency domain interpolation (FDI), for achieving high-quality speech at low bit-rates (4 kb/s and below) within reasonable complexity and delay. FDI methods, like the prototype waveform interpolation (PWI) methods, derive a prototype waveform (PW) at regular intervals of time. But, unlike PWI, there is no separation into a slowly evolving waveform (SEW) and a rapidly evolving waveform (REW) component. Instead, the PW is encoded after gain normalization in magnitude-phase form. The magnitude is modeled as a sum of mean and deviation values in multiple frequency bands and this model is quantized using switched backward adaptive VQ techniques. The phase information is represented as a composite vector of PW correlations in multiple frequency bands and an overall voicing measure. This information is quantized using a VQ at the encoder. At the decoder, a phase model is employed that uses the received phase (and magnitude) information to reproduce PWs with the correct periodicity and evolutionary characteristics. Speech is synthesized by interpolating the reconstructed PWs after gain adjustment and filtering it using the short-term predictor and a postfilter. The design of a 4-kb/s and a 2.4-kb/s FDI codec are presented in this paper and their performance is characterized in terms of delay, complexity, and subjective voice quality. The results confirm that FDI techniques have the potential for delivering high-quality speech at low bit-rates in a cost-effective manner.

Voice Morphing Using 3D Waveform Interpolation Surfaces and Lossless Tube Area Functions

Voice morphing is the process of producing intermediate or hybrid voices between the utterances of two speakers. It can also be defined as the process of gradually transforming the voice of one speaker to that of another. The ability to change the speaker's individual characteristics and to produce high-quality voices can be used in many applications. Examples include multimedia and video entertainment, as well as enrichment of speech databases in text-to-speech systems. In this study we present a new technique which enables production of a given number of intermediate voices or of utterances which gradually change from one voice to another. This technique is based on two components: (1) creation of a 3D prototype waveform interpolation (PWI) surface from the LPC residual signal, to produce an intermediate excitation signal; (2) a representation of the vocal tract by a lossless tube area function, and an interpolation of the parameters of the two speakers. The resulting synthesized signal sounds like a natural voice lying between the two original voices.

A Novel Analysis by Synthesis Approach to Prototoype Interpolation Speech Coding Based on Discrete Cosine Transform (AbS/DCT-PIC)

This paper proposes a novel Analysis by Synthesis approach to the Discrete Cosine Transform based Prototype Interpolation Coding (AbS/DCT-PIC) system that describes a second prototype for every voiced frame, which keeps the dynamics of the pitch cycles based on minimising a mean square error (MSE) criterion. This approach optimises a second set of DCT coefficients and therefore performs better than single value representation of the signal to change ratio (SCR) which prototype waveform interpolation coders employ. In addition, the proposed system delivers excellent speech quality at or above 3.6 Kb/s and is robust to pitch estimation errors.

Time domain characteristics of rational systems with scale-invariant frequency response

The class of rational systems characterized by a magnitude response which is scale invariant for a specific scale change, or equivalently γ-homogeneous rational systems, are useful in modeling power-law processes. Such systems can be constructed by cascading frequency-scaled replicas of a prototype rational function which satisfies certain conditions. In this communication, we study the time domain characteristics of such systems. We show that, in the case of degree-1 or degree-2 prototypes, the magnitudes of the partial fraction expansion coefficients constitute a geometric sequence. Furthermore, in the degree-2 case, the angles of the partial fraction expansion coefficients are equal. Using these properties, we demonstrate that the impulse response of a e, γ-homogeneous rational system is essentially a linear combination of dilations of a prototype waveform and therefore exhibits a wavelet-like decomposition.

Voiced speech coding at very low bit rates based on forward-backward waveform prediction

Techniques for coding voiced speech at very low bit rates are investigated and a new algorithm, designed to produce high quality speech with low complexity, is proposed. This algorithm encodes and transmits partial representative waveforms (RWs) from which the complete speech waveforms are reconstructed by using a method called forward-backward waveform prediction (FBWP). The RW is encoded at 20-30 ms intervals with a low complexity approach, taking into account the special initial conditions of short- and long-term filters. The basic idea of FBWP is essentially consistent with that of the prototype waveform interpolation (PWI) algorithm, which was reported to be capable of producing high-quality voiced speech at a bit rate of between 3.0 and 4.0 kb/s. By implementing the FBWP in the time domain, fast computation is thereby made possible while high-quality speech can be obtained at bit rate of about 3 kb/s. As in the PWI method, the proposed algorithm may be combined with an LP-based speech coder which uses a noise-like excitation to reproduce unvoiced speech.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Robust waveform interpolation for the entire speech signal

In waveform interpolation (WI), a speech signal is reconstructed by the concatenation of infinitesimal segments of an evolving characteristic waveform, which is obtained by interpolation over time [W. B. Kleijn, IEEE Trans. Speech Audio Process. 1, 386–399 (1993)]. WI leads to efficient coding of voiced speech, but current implementations switch to CELP for nonperiodic signals. The WI paradigm is extended to provide an effective basis for the coding of voiced and unvoiced speech and background noise. Prototype waveforms are extracted every 2.5 ms. At this high rate the WI analysis–synthesis system results in transparent speech quality. Each prototype waveform is decomposed into a slowly evolving waveform (SEW), obtained by convolution in time with a 40-ms smoothing window, and a remainder, the rapidly evolving waveform (REW). Because of its low bandwidth, a low bit rate suffices for the SEW (additional processing lowers the bit rate further), while the REW requires only a rough statistical description (e.g., its phase spectrum can be randomized). The new paradigm facilitates efficient, robust speech coding in the range 2–8 kb/s.

Encoding speech using prototype waveforms

Voiced speech is interpreted as a concentration of slowly evolving pitch-cycle waveforms. This signal can be reconstructed by interpolation from a downsampled sequence of pitch-cycle waveforms with a rate of one prototype waveform per 20-30 ms interval. The prototype waveform is described by a set of linear-prediction (LP) filter coefficients describing the formant structure and a prototype excitation waveform, quantized with analysis-by-synthesis procedures. The speech signal is reconstructed by filtering an excitation signal consisting of the concatenation of (infinitesimal) sections of the instantaneous excitation waveforms. To obtain the correct level of periodicity, the short-term and the long-term correlations between the instantaneous excitation waveforms can be controlled explicitly. Thus, distortions such as noise, reverberation, and buzziness can be prevented. The coding method is easily combined with existing LP-based speech coders, such as CELP, for unvoiced signals. Excellent voiced speech quality is obtained at rates between 3.0 and 4.0 kb/s. >

Generation of ECG prototype waveforms by piecewise correlational averaging

Describes a novel approach to the processing of ECG data. The procedure constructs a prototype waveform and generates timing statistics on the waveform components within the beats. Individual segments, defined heuristically to separate the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QRS</i> , and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> waves in each recorded waveform, are aligned by correlation and averaged independently. The individual segment averages are subsequently concatenated to form the prototype. The new prototype is shown to correlate better with the ensemble of data waveforms than does the conventional time-locked average. The process also generates intrabeat statistics that describe the relative latency fluctuations within the waveform population.