You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review 
Chapter Title:  Spectral Subtraction 
1. Abstract of Cited Paper (BIB001):  Abstract Achieving reliable performance for a speech recogniser is an important challenge, especially in the context of mobile telephony applications where the user can access telephone functions through voice. The breakthrough of such a technology is appealing, since the driver can concentrate completely and safely on his task while composing and conversing in a “full” hands-free mode. This paper addresses the problem of speaker-dependent discrete utterance recognition in noise. Special reference is made to the mismatch effects due to the fact that training and testing are made in different environments. A novel technique for noise compensation is proposed: nonlinear spectral subtraction (NSS). Robust variance estimates and robust pdf evaluations (projection) are also introduced and combined with NSS into the HMM framework. We show that the lower limit of applicability of the projection (low SNR values) can be loosened after combination with NSS. Experimental results are reported. The performance of an HMM-based recogniser rises from 56% (no compensation) to 98% after speech enhancement. More than 3300 utterances have been used to evaluate the systems (three databases, two European languages). This result is achieved by the use of robust training/recognition schemes and by preprocessing the noisy speech by NSS. 
2. Abstract of Cited Paper (BIB002):  This paper addresses the problem of single channel speech enhancement at very low signal-to-noise ratios (SNRs) (<10 dB). The proposed approach is based on the introduction of an auditory model in a subtractive-type enhancement process. Single channel subtractive-type algorithms are characterized by a tradeoff between the amount of noise reduction, the speech distortion, and the level of musical residual noise, which can be modified by varying the subtraction parameters. Classical algorithms are usually limited to the use of fixed optimized parameters, which are difficult to choose for all speech and noise conditions. A new computationally efficient algorithm is developed based on masking properties of the human auditory system. It allows for an automatic adaptation in time and frequency of the parametric enhancement system, and finds the best tradeoff based on a criterion correlated with perception. This leads to a significant reduction of the unnatural structure of the residual noise. Objective and subjective evaluation of the proposed system is performed with several noise types form the Noisex-92 database, having different time-frequency distributions. The application of objective measures, the study of the speech spectrograms, as well as subjective listening tests, confirm that the enhanced speech is more pleasant to a human listener. Finally, the proposed enhancement algorithm is tested as a front-end processor for speech recognition in noise, resulting in improved results over classical subtractive-type algorithms. 
3. Abstract of Cited Paper (BIB003):  This paper proposes a recursive parametric spectral subtraction (RPSS) algorithm based on the characteristics of the human auditory system to calculate the masking threshold. Since the masking threshold of a clean signal is difficult, if not impossible, to calculate from noisy speech, we add a recursive process to estimate the subtraction factor and the spectral floor factor of parametric spectral subtraction approaches. The recursive process is terminated when the output SNR of the current signal frame shows no further improvement. The effectiveness of the proposed RPSS has been validated by the SNR improvement test and the recognition rate test and compared with the standard power spectral subtraction (PSS) method. According to the experimental results, the proposed method outperforms the PSS method in both tests.

Literature Review:
Spectral subtraction is historically one of the first algorithms proposed in the field of background noise reduction, which is still referenced today because of its minimal complexity and relative ease in implementation. Spectral subtraction is performed by subtracting the average magnitude of the noise spectrum from the spectrum of the noisy speech, to estimate the magnitude of the enhanced speech spectrum [1] . The estimate of the enhanced speech spectrum is obtained as [1] : where D k  ( ) is the average magnitude of the noise spectrum. The noise estimation is obtained on the assumption that the background noise is locally stationary, so that the noise characteristics computed during the speech pauses are a good approximation to the noise characteristics  . The enhanced spectrum obtained using the above relation may contain some negative values due to the errors in estimating the noise spectrum. The simplest solution is half-wave rectification of these values, to ensure a non negative magnitude spectrum. This nonlinear processing of negative values creates small, isolated peaks in the spectrum occurring at random frequency locations in each of the frames. Converted in the time domain, these peaks sound similar to the tones with frequencies that change randomly from frame to frame; that is, tones that are turned on and off at the analysis frame rate. This type of noise is commonly referred as musical noise BIB001 . The musical noise can be more annoying to the listeners than the original distortion caused by the background noise. Several modifications for the standard spectral subtraction method have been proposed to alleviate the speech distortion introduced by the spectral subtraction process  . Boll [1] proposed few modifications such as magnitude averaging, residual noise reduction and additional signal attenuation during non-speech activity, to reduce the effect of musical noise. Berouti et al. BIB001 suggested a method to reduce the musical noise by subtracting an overestimate of the noise spectrum, while preventing the resultant spectral components from going below a preset minimum value. The proposed technique has the following form BIB001 : where α is the over subtraction factor, which is a function of the noisy Signal to Noise Ratio (SNR) and calculated as BIB001 : where α 0 is the desired value of α at 0 dB SNR. Here, SNR is computed as the ratio of the noisy speech power to the estimated noise power. In general, the higher the amount of over subtraction, more will be the attenuation of the stronger components with a low SNR. This prevents musical noise. However, too strong over subtraction will suppress too many components. Therefore, the value of α has to be carefully chosen, in order to prevent both the musical noise and signal distortion BIB001 . The introduction of spectral floor β prevents the subtraction of spectral components of the enhanced speech spectrum falling below the predefined lower value. A frequency adaptive subtraction factor based approach is proposed in BIB002  . The motivation is that, in general, noise may not affect the speech signal uniformly over the whole spectrum. Some frequencies are affected more severely than the others. Accordingly, Lockwood and Boudy BIB002 proposed the nonlinear spectral subtraction (NSS) method, based on the linear spectral subtraction proposed by Berouti et al. BIB001 . In NSS, the over subtraction factor is frequency dependent in each frame of speech. Larger values are subtracted at frequencies with low SNR levels and smaller values are subtracted at frequencies with high SNR levels. Kamath and Loizou  extended this concept and developed a multi-band spectral subtraction method that divides the speech spectrum into N nonoverlapping bands, and the over subtraction factor for each band is calculated independently. The individual frequency bands of the estimated noise spectrum are subtracted from the corresponding bands of the noisy speech spectrum. The performances of the above methods are not satisfactory in adverse environments, particularly when the SNR is very low. The reason is that in very low SNR conditions, it is still difficult to suppress noise without degrading intelligibility, and without introducing residual noise and speech distortion. Due to this fact, several perceptual-based approaches are advocated, wherein instead of completely eliminating the musical noise and introducing distortion, the noise is masked taking advantage of the simultaneous masking properties of the auditory system BIB004 BIB003 . The masking effect means that a stronger signal can make a weaker signal occurring simultaneously inaudible. If the noise signal is weaker than the speech signal in the same frequency band, then the noise signal is masked by the speech signal BIB004 . Therefore, we can use less noise subtraction to avoid unnecessary distortion. Accordingly, instead of attempting to remove all noise from the signal, these algorithms attempt to attenuate the noise below the audible threshold BIB004 BIB003 . The methods that adopt the masking property of the human auditory system can reduce the effect of residual noise, but the drawback is the large computational effort associated with the subband decomposition and the additional discrete Fourier transform (DFT) analyzer required for psychoacoustic modeling. Even though several improvements have been proposed, spectral subtraction approach is still a subject of many researches on how to increase its performance in terms of minimizing the effect of musical noise and also on making it suitable for non-stationary environments.