Measurement of Turbulence and pitch of the airstream in the 5'* 7' tunnel of Indian Institute of Science

Abstract is not available.

Bauer Method of MVDR Spectral Factorization for Pitch Modification in the Source Domain

In our earlier work [1], we employed MVDR (minimum variance distortionless response) based spectral estimation instead of modified-linear prediction method [2] in pitch modification. Here, we use the Bauer method of MVDR spectral factorization, leading to a causal inverse filter rather than a noncausal filter setup with MVDR spectral estimation [1]. Further, this is employed to obtain source (or residual) signal from pitch synchronous speech frames. The residual signal is resampled using DCT/IDCT depending on the target pitch scale factor. Finally, forward filters realized from the above factorization are used to get pitch modified speech. The modified speech is evaluated subjectively by 10 listeners and mean opinion scores (MOS) are tabulated. Further, modified bark spectral distortion measure is also computed for objective evaluation of performance. We find that the proposed algorithm performs better compared to time domain pitch synchronous overlap [3] and modified-LP method [2]. A good MOS score is achieved with the proposed algorithm compared to [1] with a causal inverse and forward filter setup.

Suboptimal reentry guidance of a reusable launch vehicle using pitch plane maneuver

Using the recently developed model predictive static programming (MPSP) technique, a nonlinear suboptimal reentry guidance scheme is presented in this paper for a reusable launch vehicle (RLV). Unlike traditional RLV guidance, the problem considered over here is restricted only to pitch plane maneuver of the vehicle, which allows simpler mission planning and vehicle load management. The computationally efficient MPSP technique brings in the philosophy of trajectory optimization into the framework of guidance design, which in turn results in very effective guidance schemes in general. In the problem addressed in this paper, it successfully guides the RLV through the critical reentry phase both by constraining it to the allowable narrow flight corridor as well as by meeting the terminal constraints at the end of the reentry segment. The guidance design is validated by considering possible aerodynamic uncertainties as well as dispersions in the initial conditions. (C) 2010 Elsevier Masson SAS. All rights reserved.

Extrema based unwarping for time-varying pitch estimation

We present an extrema based unwarping technique for signals with time-varying periodicity. We show that for arbitrary variation of pitch periodicity in speech signal,the unwarping technique maps the signals to periodic signals which enable eficient estimation of periodicity. We demonstrate the e�ectiveness of the new technique using both synthetic and real speech signals.

Joint pitch-analysis formant-synthesis framework for CS recovery of speech

A joint analysis-synthesis framework is developed for the compressive sensing (CS) recovery of speech signals. The signal is assumed to be sparse in the residual domain with the linear prediction filter used as the sparse transformation. Importantly this transform is not known apriori, since estimating the predictor filter requires the knowledge of the signal. Two prediction filters, one comb filter for pitch and another all pole formant filter are needed to induce maximum sparsity. An iterative method is proposed for the estimation of both the prediction filters and the signal itself. Formant prediction filter is used as the synthesis transform, while the pitch filter is used to model the periodicity in the residual excitation signal, in the analysis mode. Significant improvement in the LLR measure is seen over the previously reported formant filter estimation.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification

A characterization of the voice source (VS) signal by the pitch synchronous (PS) discrete cosine transform (DCT) is proposed. With the integrated linear prediction residual (ILPR) as the VS estimate, the PS DCT of the ILPR is evaluated as a feature vector for speaker identification (SID). On TIMIT and YOHO databases, using a Gaussian mixture model (GMM)-based classifier, it performs on par with existing VS-based features. On the NIST 2003 database, fusion with a GMM-based classifier using MFCC features improves the identification accuracy by 12% in absolute terms, proving that the proposed characterization has good promise as a feature for SID studies. (C) 2015 Acoustical Society of America