Optimized estimation of spectral parameters for the coding of noisy speech

نویسندگان

  • Rainer Martin
  • Ingo Wittke
  • Peter Jax
چکیده

In this contribution we optimize a speech enhancement preprocessor such that a distortion measure in the Line Spectral Frequency (LSF) domain is minimized. We can thus improve the estimation of spectral parameters of a speech coder when the input signal to the coder is a noisy speech signal. The optimization aims at the maximum noise reduction of the enhancement preprocessor. The average maximum noise reduction characteristic is determined as a function of the speech signal SNR and is approximated by an exponential function. Since LSF parameters are widely used in speech coding the results are applicable to a wide range of speech coders and enhancement preprocessors. We report experimental results for an MhlSE Log Spectral Amplitude estimator in conjunction with the new ETSI Adaptive Multi-Rate (AhIIR) speech coder. We found that the method is most effective for the low bit rate coding modes.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Speech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering

Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equatio...

متن کامل

Speech Enhancement Through an Optimized Subspace Division Technique

The speech enhancement techniques are often employed to improve the quality and intelligibility of the noisy speech signals. This paper discusses a novel technique for speech enhancement which is based on Singular Value Decomposition. This implementation utilizes a Genetic Algorithm based optimization method for reducing the effects of environmental noises from the singular vectors as well as t...

متن کامل

Noise power spectral density estimation based on optimal smoothing and minimum statistics

We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks spectral minima in each frequency band without any dist...

متن کامل

Speech Enhancement Through an Optimized Subspace Division Technique

The speech enhancement techniques are often employed to improve the quality and intelligibility of the noisy speech signals. This paper discusses a novel technique for speech enhancement which is based on Singular Value Decomposition. This implementation utilizes a Genetic Algorithm based optimization method for reducing the effects of environmental noises from the singular vectors as well as t...

متن کامل

Improving the performance of MFCC for Persian robust speech recognition

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000