Thickening of galactic disks through clustered star formation

(Abridged) The building blocks of galaxies are star clusters. These form with low-star formation efficiencies and, consequently, loose a large part of their stars that expand outwards once the residual gas is expelled by the action of the massive stars. Massive star clusters may thus add kinematically hot components to galactic field populations. This kinematical imprint on the stellar distribution function is estimated here by calculating the velocity distribution function for ensembles of star-clusters distributed as power-law or log-normal initial cluster mass functions (ICMFs). The resulting stellar velocity distribution function is non-Gaussian and may be interpreted as being composed of multiple kinematical sub-populations. The notion that the formation of star-clusters may add hot kinematical components to a galaxy is applied to the age--velocity-dispersion relation of the Milky Way disk to study the implied history of clustered star formation, with an emphasis on the possible origin of the thick disk.Comment: MNRAS, accepted, 27 pages, 9 figure

Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement

This paper presents a speech enhancement method based on the tracking and denoising of the formants of a linear prediction (LP) model of the spectral envelope of speech and the parameters of a harmonic noise model (HNM) of its excitation. The main advantages of tracking and denoising the prominent energy contours of speech are the efficient use of the spectral and temporal structures of successive speech frames and a mitigation of processing artefact known as the ‘musical noise’ or ‘musical tones’.The formant-tracking linear prediction (FTLP) model estimation consists of three stages: (a) speech pre-cleaning based on a spectral amplitude estimation, (b) formant-tracking across successive speech frames using the Viterbi method, and (c) Kalman filtering of the formant trajectories across successive speech frames.The HNM parameters for the excitation signal comprise; voiced/unvoiced decision, the fundamental frequency, the harmonics’ amplitudes and the variance of the noise component of excitation. A frequency-domain pitch extraction method is proposed that searches for the peak signal to noise ratios (SNRs) at the harmonics. For each speech frame several pitch candidates are calculated. An estimate of the pitch trajectory across successive frames is obtained using a Viterbi decoder. The trajectories of the noisy excitation harmonics across successive speech frames are modeled and denoised using Kalman filters.The proposed method is used to deconstruct noisy speech, de-noise its model parameters and then reconstitute speech from its cleaned parts. Experimental evaluations show the performance gains of the formant tracking, pitch extraction and noise reduction stages

Speech enhancement using harmonic- plus-noise models

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Speech enhancement using harmonic- plus-noise models

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Restoration of Noisy and Band Limited Archived Speech Records with Linear Predictor and Harmonic Noise Models

A method is presented for restoration of noisy bandlimited archived speech records. Speech is modeled with a formanttracking linear prediction (FTLP) model of the spectral envelope and a harmonic noise model (HNM) of the excitation. The timevarying trajectories of the parameters of the LP and HNM models are tracked with Viterbi classifiers and denoised with Kalman filters. A frequency domain pitch estimation is proposed, which searches for the peak SNRs at the harmonics. The LP-HNM model is used to deconstruct noisy speech, de-noise its LP and HNM models and then reconstitute the cleaned speech. The missing spectrum at lower and higher frequency bands are reconstructed through spectral extrapolation of the LP-HNM model. Comparative evaluations show the performance gains obtained from the proposed method

Kalman filter with linear predictor and harmonic noise models for noisy speech enhancement

This paper presents a method for noisy speech enhancement based on integration of a formant-tracking linear prediction (FTLP) model of spectral envelope and a harmonic noise model (HNM) of the excitation of speech. The time-varying trajectories of the parameters of the LP and HNM models are tracked with Viterbi classifiers and smoothed with Kalman filters. A frequency domain pitch estimation is proposed, that searches for the peak SNRs at the harmonics. The LP-HNM model is used to deconstruct noisy speech, de-noise its LP and HNM models and then reconstitute cleaned speech. Experimental evaluations show the performance gains resulting from the formant tracking, harmonic extraction and noise reduction stages