Speech Enhancement By Exploiting The Baseband Phase Structure Of Voiced Speech For Effective Non-Stationary Noise Estimation

Speech enhancement is one of the most important and challenging issues in the speech communication and signal processing field. It aims to minimize the effect of additive noise on the quality and intelligibility of the speech signal. Speech quality is the measure of noise remaining after the processing on the speech signal and of how pleasant the resulting speech sounds, while intelligibility refers to the accuracy of understanding speech. Speech enhancement algorithms are designed to remove the additive noise with minimum speech distortion.The task of speech enhancement is challenging due to lack of knowledge about the corrupting noise. Hence, the most challenging task is to estimate the noise which degrades the speech. Several approaches has been adopted for noise estimation which mainly fall under two categories: single channel algorithms and multiple channel algorithms. Due to this, the speech enhancement algorithms are also broadly classified as single and multiple channel enhancement algorithms.In this thesis, speech enhancement is studied in acoustic and modulation domains along with both amplitude and phase enhancement. We propose a noise estimation technique based on the spectral sparsity, detected by using the harmonic property of voiced segment of the speech. We estimate the frame to frame phase difference for the clean speech from available corrupted speech. This estimated frame-to-frame phase difference is used as a means of detecting the noise-only frequency bins even in voiced frames. This gives better noise estimation for the highly non-stationary noises like babble, restaurant and subway noise. This noise estimation along with the phase difference as an additional prior is used to extend the standard spectral subtraction algorithm. We also verify the effectiveness of this noise estimation technique when used with the Minimum Mean Squared Error Short Time Spectral Amplitude Estimator (MMSE STSA) speech enhancement algorithm. The combination of MMSE STSA and spectral subtraction results in further improvement of speech quality

Speech Enhancement for Automatic Analysis of Child-Centered Audio Recordings

Analysis of child-centred daylong naturalist audio recordings has become a de-facto research protocol in the scientific study of child language development. The researchers are increasingly using these recordings to understand linguistic environment a child encounters in her routine interactions with the world. These audio recordings are captured by a microphone that a child wears throughout a day. The audio recordings, being naturalistic, contain a lot of unwanted sounds from everyday life which degrades the performance of speech analysis tasks. The purpose of this thesis is to investigate the utility of speech enhancement (SE) algorithms in the automatic analysis of such recordings. To this effect, several classical signal processing and modern machine learning-based SE methods were employed 1) as a denoiser for speech corrupted with additive noise sampled from real-life child-centred daylong recordings and 2) as front-end for downstream speech processing tasks of addressee classification (infant vs. adult-directed speech) and automatic syllable count estimation from the speech. The downstream tasks were conducted on data derived from a set of geographically, culturally, and linguistically diverse child-centred daylong audio recordings. The performance of denoising was evaluated through objective quality metrics (spectral distortion and instrumental intelligibility) and through the downstream task performance. Finally, the objective evaluation results were compared with downstream task performance results to find whether objective metrics can be used as a reasonable proxy to select SE front-end for a downstream task. The results obtained show that a recently proposed Long Short-Term Memory (LSTM)-based progressive learning architecture provides maximum performance gains in the downstream tasks in comparison with the other SE methods and baseline results. Classical signal processing-based SE methods also lead to competitive performance. From the comparison of objective assessment and downstream task performance results, no predictive relationship between task-independent objective metrics and performance of downstream tasks was found

A study on different linear and non-linear filtering techniques of speech and speech recognition

In any signal noise is an undesired quantity, however most of thetime every signal get mixed with noise at different levels of theirprocessing and application, due to which the information containedby the signal gets distorted and makes the whole signal redundant.A speech signal is very prominent with acoustical noises like bubblenoise, car noise, street noise etc. So for removing the noises researchershave developed various techniques which are called filtering. Basicallyall the filtering techniques are not suitable for every application,hence based on the type of application some techniques are betterthan the others. Broadly, the filtering techniques can be classifiedinto two categories i.e. linear filtering and non-linear filtering.In this paper a study is presented on some of the filtering techniqueswhich are based on linear and nonlinear approaches. These techniquesincludes different adaptive filtering based on algorithm like LMS,NLMS and RLS etc., Kalman filter, ARMA and NARMA time series applicationfor filtering, neural networks combine with fuzzy i.e. ANFIS. Thispaper also includes the application of various features i.e. MFCC,LPC, PLP and gamma for filtering and recognition

A perceptual masking approach for noise robust speech recognition

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Offline and real time noise reduction in speech signals using the discrete wavelet packet decomposition

This thesis describes the development of an offline and real time wavelet based speech enhancement system to process speech corrupted with various amounts of white Gaussian noise and other different noise types

Speech enhancement by perceptual adaptive wavelet de-noising

This thesis work summarizes and compares the existing wavelet de-noising methods. Most popular methods of wavelet transform, adaptive thresholding, and musical noise suppression have been analyzed theoretically and evaluated through Matlab simulation. Based on the above work, a new speech enhancement system using adaptive wavelet de-noising is proposed. Each step of the standard wavelet thresholding is improved by optimized adaptive algorithms. The Quantile based adaptive noise estimate and the posteriori SNR based threshold adjuster are compensatory to each other. The combination of them integrates the advantages of these two approaches and balances the effects of noise removal and speech preservation. In order to improve the final perceptual quality, an innovative musical noise analysis and smoothing algorithm and a Teager Energy Operator based silent segment smoothing module are also introduced into the system. The experimental results have demonstrated the capability of the proposed system in both stationary and non-stationary noise environments

The Application of Nonlinear Spectral Subtraction Method on Millimeter Wave Conducted Speech Enhancement

A nonlinear multiband spectral subtraction method is investigated in this study to reduce the colored electronic noise in millimeter wave (MMW) radar conducted speech. Because the over-subtraction factor of each Bark frequency band can be adaptively adjusted, the nonuniform effects of colored noise in the spectrum of the MMW radar speech can be taken into account in the enhancement process. Both the results of the time-frequency distribution analysis and perceptual evaluation test suggest that a better whole-frequency noise reduction effect is obtained, and the perceptually annoying musical noise was efficiently reduced, with little distortion to speech information as compared to the other standard speech enhancement algorithm

Evaluation of the sparse coding shrinkage noise reduction algorithm for the hearing impaired

Although there are numerous single-channel noise reduction strategies to improve speech perception in a noisy environment, most of them can only improve speech quality but not improve speech intelligibility for normal hearing (NH) or hearing impaired (HI) listeners. Exceptions that can improve speech intelligibility currently are only those that require a priori statistics of speech or noise. Most of the noise reduction algorithms in hearing aids are adopted directly from the algorithms for NH listeners without taking into account of the hearing loss factors within HI listeners. HI listeners suffer more in speech intelligibility than NH listeners in the same noisy environment. Further study of monaural noise reduction algorithms for HI listeners is required.The motivation is to adapt a model-based approach in contrast to the conventional Wiener filtering approach. The model-based algorithm called sparse coding shrinkage (SCS) was proposed to extract key speech information from noisy speech. The SCS algorithm was evaluated by comparison with another state-of-the-art Wiener filtering approach through speech intelligibility and quality tests using 9 NH and 9 HI listeners. The SCS algorithm matched the performance of the Wiener filtering algorithm in speech intelligibility and speech quality. Both algorithms showed some intelligibility improvements for HI listeners but not at all for NH listeners. The algorithms improved speech quality for both HI and NH listeners.Additionally, a physiologically-inspired hearing loss simulation (HLS) model was developed to characterize hearing loss factors and simulate hearing loss consequences. A methodology was proposed to evaluate signal processing strategies for HI listeners with the proposed HLS model and NH subjects. The corresponding experiment was performed by asking NH subjects to listen to unprocessed/enhanced speech with the HLS model. Some of the effects of the algorithms seen in HI listeners are reproduced, at least qualitatively, by using the HLS model with NH listeners.Conclusions: The model-based algorithm SCS is promising for improving performance in stationary noise although no clear difference was seen in the performance of SCS and a competitive Wiener filtering algorithm. Fluctuating noise is more difficult to reduce compared to stationary noise. Noise reduction algorithms may perform better at higher input signal-to-noise ratios (SNRs) where HI listeners can get benefit but where NH listeners already reach ceiling performance. The proposed HLS model can save time and cost when evaluating noise reduction algorithms for HI listeners