Nasir Saleem

Noise Reduction Based on Soft Masks by Incorporating SNR Uncertainty in Frequency Domain

The binary mask approach has been studied recently to reduce the background noise and improve the speech intelligibility and quality in the noisy surroundings. This mask is usually applied at the time–frequency illustration of a noisy speech and discards portions of a speech below a signal-to-noise-ratio (SNR) threshold, whereas allowing others to pass over intact. The threshold, however, is normally very low, and considerable residual noise would exist. Moreover, the precise estimate of local instantaneous SNR in practical applications is a difficult task. By modeling the local instantaneous SNR as Fisher–Snedecor distributed random variable, the soft masks for noise reduction are derived by incorporating SNR uncertainty in the frequency domain. Instead of finding a different method to estimate the local instantaneous SNR, the probability of local instantaneous SNR is computed higher than the threshold. The results indicated that soft masks yielded significantly better speech quality in terms of speech distortion and residual noise.

Single channel noise reduction system in low SNR

We propose a two stage noise reduction system for reducing background noise using single-microphone recordings in very low signal-to-noise ratio (SNR) based on Wiener filtering and ideal binary masking. The proposed system contains two stages. In first stage, the Wiener filtering with improved a priori SNR is applied to noisy speech for background noise reduction. In second stage, the ideal binary mask is estimated at every time–frequency channel by using pre-processed first stage speech and comparing the time–frequency channels against a pre-selected threshold T to reduce the residual noise. The time–frequency channels satisfying the threshold are preserved whereas all other time–frequency channels are attenuated. The results revealed substantial improvements in speech intelligibility and quality over that accomplished with the traditional noise reduction algorithms and unprocessed speech.

Low rank sparse decomposition model based speech enhancement using gammatone filterbank and Kullback–Leibler divergence

In speech enhancement systems, the key stage is to estimate noise which generally requires prior speech or noise models. However, it is difficult to obtain such prior models sometimes. This paper presents a speech enhancement algorithm which does not require prior knowledge of speech and noise, and is based on low-rank and sparse matrix decomposition model using gammatone filterbank and Kullback–Leibler divergence to estimate noise and speech by decomposing the input noisy speech magnitude spectra into low-rank noise and sparse speech parts, respectively. According to the proposed technique, noise signals are assumed as low-rank components because noise spectra within different time frames are usually highly correlated with each other; while the speech signals are considered as sparse components because they are relatively sparse in time–frequency domain. Based on these assumptions, we have developed an alternative speech enhancement algorithm to separate the speech and noise magnitude spectra by imposing rank and sparsity constraints, with which the enhanced time-domain speech can be constructed from sparse matrix The proposed technique is significantly different from existing speech enhancement techniques as it enhances noisy speech in an uncomplicated manner, without need of noise estimation algorithm to find noise-only excerpts for noise estimation. Moreover, it can obtain improved performance in low SNR conditions, and does not need to know the exact distribution of noise signals. Experimental results have showed that proposed technique can perform better than conventional techniques in many types of strong noise conditions, in terms of yielding less residual noise, lower speech distortion and better overall speech quality. An important improvement in terms of the PESQ, SNRSeg, SIG and BAK is observed with the proposed algorithm over baseline algorithms.