Concept

Linear prediction

Related publications (31)

Autoregressive Modelling of Hilbert Envelopes for Wide-band Audio Coding

Petr Motlicek, Hynek Hermansky, Sriram Ganapathy

Frequency Domain Linear Prediction (FDLP) represents the technique for approximating temporal envelopes of a signal using autoregressive models. In this paper, we propose a wide-band audio coding system exploiting FDLP. Specifically, FDLP is applied on cri ...

IDIAP2008

Spectral Noise Shaping: Improvements in Speech/Audio Codec Based on Linear Prediction in Spectral Domain

Petr Motlicek, Hynek Hermansky, Sriram Ganapathy

Audio coding based on Frequency Domain Linear Prediction (FDLP) uses auto-regressive models to approximate Hilbert envelopes in frequency sub-bands. Although the basic technique achieves good coding efficiency, there is a need to improve the reconstructed ...

2008

Spectral Noise Shaping: Improvements in Speech/Audio Codec Based on Linear Prediction in Spectral Domain

Petr Motlicek, Hynek Hermansky, Sriram Ganapathy

IDIAP2008

Spectro-Temporal Features for Automatic Speech Recognition using Linear Prediction in Spectral Domain

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

Frequency Domain Linear Prediction (FDLP) provides an efficient way to represent temporal envelopes of a signal using auto-regressive models. For the input speech signal, we use FDLP to estimate temporal trajectories of sub-band energy by applying linear p ...

2008

Spectro-Temporal Features for Automatic Speech Recognition using Linear Prediction in Spectral Domain

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

IDIAP2008

Recognition Of Reverberant Speech Using Frequency Domain Linear Prediction

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

Performance of a typical automatic speech recognition (ASR) system severely degrades when it encounters speech from reverberant environments. Part of the reason for this degradation is the feature extraction techniques that use analysis windows which are m ...

2008

Recognition Of Reverberant Speech Using Frequency Domain Linear Prediction

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

IDIAP2008

Generalized linear prediction method in phase-shifting interferometry in the presence of noise

Pramod Rastogi, Abhijit Patil, Rajesh Langoju

The effectiveness of phase-shifting interferometry (PSI) techniques employing piezoelectric device PZT in the estimation of phase depends largely on the accuracy with which the phase shifts are imparted to the device and the noise influencing the measureme ...

Elsevier2007

Novel speech processing techniques for robust automatic speech recognition

Vivek Tyagi

The goal of this thesis is to develop and design new feature representations that can improve the automatic speech recognition (ASR) performance in clean as well noisy conditions. One of the main shortcomings of the fixed scale (typically 20-30 ms long ana ...

EPFL2006

Efficient Kalman Smoothing for Harmonic State-Space Models

Harmonic probabilistic models are common in signal analysis. Framed as a linear-Gaussian state-space model, smoothed inference scales as

O(TH^2)

where

H

is twice the number of frequencies in the model and

T

is the length of the time-series. Due to th ...

IDIAP2005

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Efficient Kalman Smoothing for Harmonic State-Space Models

Harmonic probabilistic models are common in signal analysis. Framed as a linear-Gaussian state-space model, smoothed inference scales as

O(TH^2)

where

H

is twice the number of frequencies in the model and

T

is the length of the time-series. Due to th ...

IDIAP2005