A new weighted NMF algorithm for missing data interpolation and its application to speech enhancement

In this paper we present a novel weighted NMF (WNMF) algorithm for interpolating missing data. The proposed approach has a computational cost equivalent to that of standard NMF and, additionally, has the flexibility to control the degree of interpolation in the missing data regions. Existing WNMF methods do not offer this capability and, thereby, tend to overestimate the values in the masked regions. By constraining the estimates of the missing-data regions, the proposed approach allows for a better trade-off in the interpolation. We further demonstrate the applicability of WNMF and missing data estimation to the problem of speech enhancement. In this preliminary work, we consider the improvement obtainable by applying the proposed method to ideal binary mask-based gain functions. The instrumental quality metrics (PESQ and SNR) clearly indicate the added benefit of the missing data interpolation, compared to the output of the ideal binary mask. This preliminary work opens up novel possibilities not only in the field of speech enhancement but also, more generally, in the field of missing data interpolation using NMF

Speech Enhancement using Hmm and Snmf(Os)

The speech enhancement is the process to enhance the speech signal by reducing the noise from the signal as well as improving the quality of the signal. The speech signal enhancement requires various techniques associated with the signal noise removal as well as the signal patch fixation in order to enhance the frequency of the speech signal. In this paper, we have proposed the new speech enhancement model for the speech enhancement with the amalgamation of the various speech processing techniques together. The proposed model is equipped with the Supervised sparse non-negative matrix factorization (S-SNMF) along with hidden markov model (HMM) and noise reducing filter to overcome the problem of the signal enhancement by reducing the missing values and by enhancing the signal on the weak points detected under the application of the HMM. The experimental results have proved the efficiency of the proposed model in comparison with the existing model. The improvement of nearly 50% has been recorded from the parameters of peak signal to noise ratio (PSNR), mean squared error (MSE), signal to noise ratio (SNR) etc