Fast recursive low-rank linear prediction frequency estimation algorithms

1996 ◽  
Vol 44 (4) ◽  
pp. 834-847 ◽  
Author(s):  
P. Strobach
Keyword(s):  
Linear Prediction ◽  
Frequency Estimation ◽  
Low Rank ◽  
Estimation Algorithms

Formant frequency estimation of high-pitched vowels using weighted linear prediction

2013 ◽  
Vol 134 (2) ◽  
pp. 1295-1313 ◽  
Author(s):  
Paavo Alku ◽  
Jouni Pohjalainen ◽  
Martti Vainio ◽  
Anne-Maria Laukkanen ◽  
Brad H. Story
Keyword(s):  
Linear Prediction ◽  
Frequency Estimation ◽  
Formant Frequency

scholarly journals Recovery analysis of damped spectrally sparse signals and its relation to MUSIC

2020 ◽  
Author(s):  
Shuang Li ◽  
Hassan Mansour ◽  
Michael B Wakin
Keyword(s):  
Classical Music ◽  
Frequency Estimation ◽  
Low Rank ◽  
Sparse Signals ◽  
Music Algorithm ◽  
Matrix Recovery ◽  
Norm Minimization ◽  
Rank Matrix ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

Abstract One of the classical approaches for estimating the frequencies and damping factors in a spectrally sparse signal is the MUltiple SIgnal Classification (MUSIC) algorithm, which exploits the low-rank structure of an autocorrelation matrix. Low-rank matrices have also received considerable attention recently in the context of optimization algorithms with partial observations, and nuclear norm minimization (NNM) has been widely used as a popular heuristic of rank minimization for low-rank matrix recovery problems. On the other hand, it has been shown that NNM can be viewed as a special case of atomic norm minimization (ANM), which has achieved great success in solving line spectrum estimation problems. However, as far as we know, the general ANM (not NNM) considered in many existing works can only handle frequency estimation in undamped sinusoids. In this work, we aim to fill this gap and deal with damped spectrally sparse signal recovery problems. In particular, inspired by the dual analysis used in ANM, we offer a novel optimization-based perspective on the classical MUSIC algorithm and propose an algorithm for spectral estimation that involves searching for the peaks of the dual polynomial corresponding to a certain NNM problem, and we show that this algorithm is in fact equivalent to MUSIC itself. Building on this connection, we also extend the classical MUSIC algorithm to the missing data case. We provide exact recovery guarantees for our proposed algorithms and quantify how the sample complexity depends on the true spectral parameters. In particular, we provide a parameter-specific recovery bound for low-rank matrix recovery of jointly sparse signals rather than use certain incoherence properties as in existing literature. Simulation results also indicate that the proposed algorithms significantly outperform some relevant existing methods (e.g., ANM) in frequency estimation of damped exponentials.

An Encapsulation of Vital Non-Linear Frequency Features for Various Speech Applications

2020 ◽  
Vol 17 (1) ◽  
pp. 303-307
Author(s):  
S. Lalitha ◽  
Deepa Gupta
Keyword(s):  
Speech Recognition ◽  
Linear Prediction ◽  
Performance Metrics ◽  
Speaker Identification ◽  
Frequency Estimation ◽  
Mel Frequency Cepstral Coefficients ◽  
Environment Type ◽  
Perceptual Linear Prediction ◽  
Frequency Features ◽  
Selection Of

Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual linear prediction coefficients (PLPCs) are widely casted nonlinear vocal parameters in majority of the speaker identification, speaker and speech recognition techniques as well in the field of emotion recognition. Post 1980s, significant exertions are put forth on for the progress of these features. Considerations like the usage of appropriate frequency estimation approaches, proposal of appropriate filter banks, and selection of preferred features perform a vital part for the strength of models employing these features. This article projects an overview of MFCC and PLPC features for different speech applications. The insights such as performance metrics of accuracy, background environment, type of data, and size of features are inspected and concise with the corresponding key references. Adding more to this, the advantages and shortcomings of these features have been discussed. This background work will hopefully contribute to floating a heading step in the direction of the enhancement of MFCC and PLPC with respect to novelty, raised levels of accuracy, and lesser complexity.

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