Enhancing speech using an Adaptive Wiener Filter based algorithm

In this paper, we present an implementation of adaptive Wiener filtering as a speech enhancement technique for the pitch detection purpose from speech signals affected by noise. The adaptive Wiener filtering is used as a pre-processing stage for speech enhancement, and then a combined technique from Auto-Correlation Function (ACF) and Average Magnitude Difference Function (AMDF) is implemented to get accurate results. The main objective is to improve the process of detecting the fundamental frequency of the speech signal. The adaptive Wiener filter shows a superiority in the proposed pitch detection method as compared to the traditional Wiener filterandspectral subtraction.

Speech signals are complex in nature with respect to other forms of communication media such as text or image. Different forms of noises (e.g., additive noise, channel noise, babble noise) interfere with the speech signals and drastically hamper the quality of the speech in the noisy speech signals. Enhancement of speech signals is a daunting task considering multiple forms of noises while denoising speech signals. Certain analog noise eliminator models have been studied over the years for this purpose. Researchers have also delved into some deep learning techniques to enhance speech signals. In this study, a speech enhancement system is investigated using Convolutional Denoising Autoencoder (CDAE). It takes advantages from the 2D structured inputs of the features extracted from speech signals and also considers the local temporal relationship among the features. In the proposed system, CDAE is trained considering features from noisy speech signal as input and clean speech features as desired output. The proposed CDAE based method has been tested on a benchmark dataset, called Speech Command Dataset, and attained 80% similarity between denoised speech and actual clean speech. The proposed system achieved perceptual evaluation of speech quality (PESQ) value of 2.43 which outperformed other related existing methods.

An improved Wiener filtering method for reducing background noise added to speech in colored noise environments like car engine is proposed in this paper. This implementation uses the crosscorrelartion between the speech and noise signals. The noise signal does not affect the speech signal uniformly over the whole spectrum. A nonlinear multi-band Bark scale frequency spacing approach was proposed to reduce colored noise. However, classic short-time noise reduction techniques, including this Wiener filter, introduce some residual noise that remains in the enhanced speech signal. To reduce residual noise the proposed Wiener filter is combined with constrained perceptual weighting filter. Objective and subjective tests demonstrated the proposed technique can improve the perceptual quality of speeches.

Chapter 2 - Recursive noise estimation-based Wiener filtering for monaural speech enhancement

This paper proposes an unsupervised DNN-based speech enhancement approach founded on deep priors (DPs). Here, DP signifies that DNNs are more inclined to produce clean speech signals than noises. Conventional methods based on DP typically involve training on a noisy speech signal using a random noise feature as input, stopping training only a clean speech signal is generated. However, such conventional approaches encounter challenges in determining the optimal stop timing, experience performance degradation due to environmental background noise, and suffer a trade-off between distortion of the clean speech signal and noise reduction performance. To address these challenges, we utilize two DNNs: one to generate a clean speech signal and the other to generate noise. The combined output of these networks closely approximates the noisy speech signal, with a loss term based on spectral kurtosis utilized to separate the noisy speech signal into a clean speech signal and noise. The key advantage of this method lies in its ability to circumvent trade-offs and early stopping problems, as the signal is decomposed by enough steps. Through evaluation experiments, we demonstrate that the proposed method outperforms conventional methods in the case of white Gaussian and environmental noise while effectively mitigating early stopping problems.

The complexity of the speech enhancement problem has motivated many different solutions. However, most techniques address situations in which the target speech is fully intelligible and the background noise energy is low in comparison with that of the speech. Thus while current enhancement algorithms can improve the perceived quality, the intelligibility of the speech is not increased significantly and may even be reduced. Recent research shows that intelligibility of very noisy speech can be improved by the use of a binary mask, in which a binary weight is applied to each time-frequency bin of the input spectrogram. There are several alternative goals for the binary mask estimator, based either on the Signalto-Noise Ratio (SNR) of each time-frequency bin or on the speech signal characteristics alone. Our approach to the binary mask estimation problem aims to preserve the important speech cues independently of the noise present by identifying time-frequency regions that contain significant speech energy. The speech power spectrum varies greatly for different types of speech sound. The energy of voiced speech sounds is concentrated in the harmonics of the fundamental frequency while that of unvoiced sounds is, in contrast, distributed across a broad range of frequencies. To identify the presence of speech energy in a noisy speech signal we have therefore developed two detection algorithms. The first is a robust algorithm that identifies voiced speech segments and estimates their fundamental frequency. The second detects the presence of sibilants and estimates their energy distribution. In addition, we have developed a robust algorithm to estimate the active level of the speech. The outputs of these algorithms are combined with other features estimated from the noisy speech to form the input to a classifier which estimates a mask that accurately reflects the time-frequency distribution of speech energy even at low SNR levels. We evaluate a mask-based speech enhancer on a range of speech and noise signals and demonstrate a consistent increase in an objective intelligibility measure with respect to noisy speech.

This paper proposes a Variational Mode Decomposition (VMD) based approach for enhancement of speech signals distorted by white Gaussian noise. VMD is a data adaptive method which decomposes the signal into intrinsic mode functions (IMFs) by using the Alternating Direction Method of Multipliers (ADMM). Each IMF or mode will contain a center frequency and its harmonics. This paper tries to explore VMD as a Speech enhancement technique. In the proposed method, the noisy speech signal is decomposed into IMFs using VMD. The noisy IMFs are enhanced using two methods; VMD based wavelet shrinkage (VMD-WS) and VMD based MMSE log STSA (VMD-MMSE). The speech signal distorted with different noise levels are enhanced using the VMD based methods. The level of noise reduction and speech signal quality are measured using the objective quality measures.

Noise estimation and suppression is very important for improving the quality of speech signal. Noises exist in almost all places. In reality, more than one noise degrades the speech signal. It is hard to find and supress various types of noise that affect the speech quality. This paper proposed a method for noise estimation of mixed non-stationary noisy speech signal. This method uses Spectral properties of the noisy speech signal to detect the frequency regions of noise signal. Highest frequency of speech signal is calculated and it is considered as the threshold value for separating noise signal and clean speech signal. Using Spectral subtraction, Standard deviation of noise spectrum is subtracted with noisy spectrum to acquire enhanced speech signal. Performance of the method is evaluated using SNR and Spectrogram. The main focus of this paper is to propose an independent method which estimates the noise of any type and nature.

Diffusion models are a new class of generative models that have shown outstanding performance in image generation literature. As a consequence, studies have attempted to apply diffusion models to other tasks, such as speech enhancement. A popular approach in adapting diffusion models to speech enhancement consists in modelling a progressive transformation between the clean and noisy speech signals. However, one popular diffusion model framework previously laid in image generation literature did not account for such a transformation towards the system input, which prevents from relating the existing diffusion-based speech enhancement systems with the aforementioned diffusion model framework. To address this, we extend this framework to account for the progressive transformation between the clean and noisy speech signals. This allows us to apply recent developments from image generation literature, and to systematically investigate design aspects of diffusion models that remain largely unexplored for speech enhancement, such as the neural network preconditioning, the training loss weighting, the stochastic differential equation (SDE), or the amount of stochasticity injected in the reverse process. We show that the performance of previous diffusion-based speech enhancement systems cannot be attributed to the progressive transformation between the clean and noisy speech signals. Moreover, we show that a proper choice of preconditioning, training loss weighting, SDE and sampler allows to outperform a popular diffusion-based speech enhancement system while using fewer sampling steps, thus reducing the computational cost by a factor of four.

This paper presents the improvements in the combined solution for the noise estimation and the speech enhancement in digital hearing aids in time domain. This study focuses on the single channel statistical temporal speech enhancement using adaptive Wiener filtering. In this technique, the noise is updated based on the short-term uncleaned signal to noise threshold ratio (ST-USNTR) of the frame. It works best if and only if the back ground noise level is low compared to that of speech of interest. We considered the time domain algorithms in order to consider the time varying nature of speech signal. The performance of the proposed algorithm is evaluated for speech signal with seven ty pes of noises and three signal to noise ratios (SNR) levels in each type of noise. From the results, it is clear that the basic level of adaptive speech enhancement is obtained using statistical parameters of noisy speech without the need for reference input.

A speech enhancement system for noisy speech signals under different environments, namely airport, babble, car, exhibition, restaurant, station, street and train, is designed and implemented in this paper. Spectral subtraction is the most popular method for speech enhancement which suffers from perceptually annoying musical noise. Musical noise is reduced here by using low variance spectrum estimators and enhancement is achieved by wavelet thresholding the multitaper spectrum of the speech. The main feature of the proposed system is a novel switching mechanism to an optimal wavelet filter bank or wavelet packet filter bank matching to the critical bands of the human ear, based on the type of noise present in the input speech. The system is evaluated using noisy speech signals under different environment noises at different signal to noise ratios. The subjective and objective test results show that the proposed method improves the quality and intelligibility of speech signals.

Speech signal carries much more information than spoken words and speaker’s information. Noise present in the speech signal is an obstacle in the path of analysing the speech signal precisely. Therefore, speech signal denoising has an important role in the speech signal analysis. In this paper, Wavelets are explored for pre-processing and suppression of noise from the noisy speech signals. The main aim of the proposed methodology involves, the pre-processing of the speech signals by using the combination of Discrete Wavelet Transform (DWT) and hard thresholding and to evaluate the result to find the optimal wavelet family with there optimal order followed by the suitable level of decomposition. This paper involves the exploration of the Wavelet Family and an improved Signal to Noise Ratio (SNR) values of the acquired speech signal which is shown in the result analysis. KeywordsSpeech signalsDiscrete wavelet transform (DWT)Wavelet transform (WT)Hard thresholdingSignal to noise ratio (SNR)

Forensic speaker verification systems show severe performance degradation in the presence of noise when the signal to noise ratio (SNR) is low. A possible solution to this problem is the use of multi-run independent component analysis (ICA) to reduce the effect of noise from the noisy speech signals. Previous works have used multi-run ICA in biosignal application; however, the effectiveness of multi-run ICA on noisy speaker verification has not been investigated yet. In this paper, we use multi-run ICA to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. We use a combination of feature-warped mel frequency cepstral coefficients (MFCCs) and feature-warped MFCC extracted from the discrete wavelet transform (DWT) of the enhanced speech signals as the feature extraction. A state-of-the-art identity vector (i-vector) probabilistic linear discriminant analysis (PLDA) was used as a classifier in this paper. Experimental results demonstrate that the proposed method with multi-run ICA achieves high improvements in equal error rate (EER) of 66.68%, 69.24% and 70.78% over the baseline noisy speaker verification system, when the test speech signals are corrupted with CAR, STREET, and HOME noises respectively at −10 dB SNR.

We present an adaptive enhancement method for speech signal corrupted by the 3 Tesla (3T) functional Magnetic Resonance Imaging (fMRI) acoustic noise. The noisy speech signal is filtered into several frequency bands and noise cancellation is applied to each band independently. We use Normalized Least Mean Squares (NLMS) algorithm for updating the co-efficients of the adaptive filters in both wideband and in multi-band implementations. The NLMS algorithm is used to ensure faster convergence and stability. The multi-band method shows a better performance than the conventional wideband method resulting in a better noise reduction. It is shown that the distortion introduced by the band-pass filters limits the overall speech enhancement when the number of bands increases beyond an optimal value.

The removal of noise from speech signals has applications ranging from speech enhancement for cellular communications to front ends for speech recognition systems. In this paper, we present a new nonlinear time-domain method called Noise-Regularized Adaptive Filtering. The approach is based on minimum mean-squared estimation using a modified cost function and allows designing both linear and nonlinear filters using only the observed noisy speech. 1. A GENERAL FRAMEWORK FOR MMSE ESTIMATION Given a noisy speech signal,