Studies in Signal Processing Techniques for Speech Enhancement: A comparative study

Speech enhancement is very essential to suppress the background noise and to increase speech intelligibility and reduce fatigue in hearing. There exist many simple speech enhancement algorithms like spectral subtraction to complex algorithms like Bayesian Magnitude estimators based on Minimum Mean Square Error (MMSE) and its variants. A continuous research is going and new algorithms are emerging to enhance speech signal recorded in the background of environment such as industries, vehicles and aircraft cockpit. In aviation industries speech enhancement plays a vital role to bring crucial information from pilot’s conversation in case of an incident or accident by suppressing engine and other cockpit instrument noises. In this work proposed is a new approach to speech enhancement making use harmonic wavelet transform and Bayesian estimators. The performance indicators, SNR and listening confirms to the fact that newly modified algorithms using harmonic wavelet transform indeed show better results than currently existing methods. Further, the Harmonic Wavelet Transform is computationally efficient and simple to implement due to its inbuilt decimation-interpolation operations compared to those of filter-bank approach to realize sub-bands

Speech Enhancement Using Bayesian Estimators of the Perceptually-Motivated Short-Time Spectral Amplitude (STSA) with Chi Speech Priors

In this paper, the authors propose new perceptually-motivated Weighted Euclidean (WE) and Weighted Cosh (WCOSH) estimators that utilize more appropriate Chi statistical models for the speech prior with Gaussian statistical models for the noise likelihood. Whereas the perceptually-motivated WE and WCOSH cost functions emphasized spectral valleys rather than spectral peaks (formants) and indirectly accounted for auditory masking effects, the incorporation of the Chi distribution statistical models demonstrated distinct improvement over the Rayleigh statistical models for the speech prior. The estimators incorporate both weighting law and shape parameters on the cost functions and distributions. Performance is evaluated in terms of the Segmental Signal-to-Noise Ratio (SSNR), Perceptual Evaluation of Speech Quality (PESQ), and Signal-to-Noise Ratio (SNR) Loss objective quality measures to determine the amount of noise reduction along with overall speech quality and speech intelligibility improvement. Based on experimental results across three different input SNRs and eight unique noises along with various weighting law and shape parameters, the two general, less-complicated, closed-form derived solution estimators of WE and WCOSH with Chi speech priors provide significant gains in noise reduction and noticeable gains in overall speech quality and speech intelligibility improvements over the baseline WE and WCOSH with the standard Rayleigh speech priors. Overall, the goal of the work is to capitalize on the mutual benefits of the WE and WCOSH cost functions and Chi distributions for the speech prior to improvement enhancement

Acoustic echo and noise canceller for personal hands-free video IP phone

This paper presents implementation and evaluation of a proposed acoustic echo and noise canceller (AENC) for videotelephony-enabled personal hands-free Internet protocol (IP) phones. This canceller has the following features: noise-robust performance, low processing delay, and low computational complexity. The AENC employs an adaptive digital filter (ADF) and noise reduction (NR) methods that can effectively eliminate undesired acoustic echo and background noise included in a microphone signal even in a noisy environment. The ADF method uses the step-size control approach according to the level of disturbance such as background noise; it can minimize the effect of disturbance in a noisy environment. The NR method estimates the noise level under an assumption that the noise amplitude spectrum is constant in a short period, which cannot be applied to the amplitude spectrum of speech. In addition, this paper presents the method for decreasing the computational complexity of the ADF process without increasing the processing delay to make the processing suitable for real-time implementation. The experimental results demonstrate that the proposed AENC suppresses echo and noise sufficiently in a noisy environment; thus, resulting in natural-sounding speech