Fast MinMax energy-based phase correction method for NMR spectra with linear phase distortion.

This paper addresses the problem of phase correction of dense NMR spectra on the example of the etoxy derivative of the fused heterocyclic system 5,6,10b-triazaacephenanthrylene (TAAP-OEt). A new estimation method for the linear phase correction coefficients is proposed that successfully extends the min-max (minimization of maximum errors) approach of Siegel (1981). Distinctive to the Siegel method, the smallest values of the real part of the discrete Fourier transform (DFT) spectrum are maximized, not for the whole spectrum but only for DFT bins near the peaks selected by anew energy-based criterion. Additionally, the method makes use of two one-parameter optimizations for finding the phase correction line coefficients and not the single two-parameter search. The new method is demonstrated to be precise, fast and robust against additive noise. The method's properties are verified in comparison with the state-of-the-art algorithms of Chen et al. (2002) and Bao et al. (2013) for laboratory recorded TAAP-OEt FID data and for simulated TAAP-OEt signal consisting of the sum of more than 100 complex damped exponentials. Extensive simulations were also conducted on the set of test signals derived from the TAAP-OEt signal by deterministic and pseudorandom manipulation of its content. The components of the signal model were identified by the Bertocco-Yoshida Interpolated DFT (IpDFT) algorithm with a spectral leakage correction. Simulated signals were embedded in the additive Gaussian noise, and the noise-robustness of all of the algorithms was evaluated. The obtained results demonstrate that the proposed method outperforms the Chen and the Bao algorithms, being more than 100 times faster than the Bao method (for a signal having 216 samples).

Joint application of audio spectral envelope and tonality index in an e-asthma monitoring system.

This paper presents in detail a recently introduced highly efficient method for automatic detection of asthmatic wheezing in breathing sounds. The fluctuation in the audio spectral envelope (ASE) from the MPEG-7 standard and the value of the tonality index (TI) from the MPEG-2 Audio specification are jointly used as discriminative features for wheezy sounds, while the support vector machine (SVM) with a polynomial kernel serves as a classifier. The advantages of the proposed approach are described in the paper (e.g., detecting weak wheezes, very good ROC characteristics, independence from noise color). Since the method is not computationally complex, it is suitable for remote asthma monitoring using mobile devices (personal medical assistants). The main contribution of this paper consists of presenting all the implementation details concerning the proposed approach for the first time, i.e., the pseudocode of the method and adjusting the values of the ASE and TI parameters after which only one (not two) FFT is required for analysis of a next overlapping signal fragment. The efficiency of the method has also been additionally confirmed by the AdaBoost classifier with a built-in mechanism to feature ranking, as well as a previously performed minimal-redundancy-maximal-relevance test.

Automatic recognition of pathological phoneme production.

OBJECTIVE: Proper diagnosis and therapy of pathological pronunciation of phonemes play an important role in modern logopedics. To enhance the efficiency of diagnosis and therapy an automatic recognition of pathological phoneme pronunciation is addressed in this paper. The authors focus on the therapy of phoneme substitution disorders. PATIENTS AND METHODS: Recognized speech samples come from speech-impaired Polish children and partially from persons imitating speech disorders. Recognized speech disorders were substitutions in pairs (for the correct phonetic charactors please see online article) embedded in Polish carrier words. In order to detect substitutions in the recognized words, recently proposed human factor cepstral coefficients (HFCC) have been implemented. Efficiency of the HFCC approach was compared to the application of standard mel-frequency cepstral coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patterns, and hidden Markov models (HMM) were used as classifiers. The HMM classifier was based on whole-word models as well as phoneme models. Results present a comparative analysis of DTW and HMM methods. CONCLUSIONS: The superiority of HFCC features over those of MFCC was demonstrated. Results obtained by DTW methods, mainly by modified phoneme-based DTW classifier, were slightly better in comparison with the HMM classifier. Results obtained for the detection of substitution in pairs (for the correct phonetic charactors please see online article) are very promising. The methods developed for these cases can be integrated into computer systems for speech therapy. For substitutions in pairs (for the correct phonetic charactors please see online article) further research is necessary.