Single-Microphone Speech Dereverberation based on Multiple-Step Linear Predictive Inverse Filtering and Spectral Subtraction

Single-channel speech dereverberation is a challenging problem of deconvolution of reverberation, produced by the room impulse response, from the speech signal, when only one observation of the reverberant signal (one microphone) is available. Although reverberation in mild levels is helpful in perceiving the speech (or any audio) signal, the adverse effect of reverberation, particularly at high levels, could both deteriorate the performance of automatic recognition systems and make it less intelligible by humans. Single-microphone speech dereverberation is more challenging than multi-microphone speech dereverberation, since it does not allow for spatial processing of different observations of the signal. A review of the recent single-channel dereverberation techniques reveals that, those based on LP-residual enhancement are the most promising ones. On the other hand, spectral subtraction has also been effectively used for dereverberation particularly when long reflections are involved. By using LP-residuals and spectral subtraction as two promising tools for dereverberation, a new dereverberation technique is proposed. The first stage of the proposed technique consists of pre-whitening followed by a delayed long-term LP filtering whose kurtosis or skewness of LP-residuals is maximized to control the weight updates of the inverse filter. The second stage consists of nonlinear spectral subtraction. The proposed two-stage dereverberation scheme leads to two separate algorithms depending on whether kurtosis or skewness maximization is used to establish a feedback function for the weight updates of the adaptive inverse filter. It is shown that the proposed algorithms have several advantages over the existing major single-microphone methods, including a reduction in both early and late reverberations, speech enhancement even in the case of very high reverberation time, robustness to additive background noise, and introducing only a few minor artifacts. Equalized room impulse responses by the proposed algorithms have less reverberation times. This means the inverse-filtering by the proposed algorithms is more successful in dereverberating the speech signal. For short, medium and high reverberation times, the signal-to-reverberation ratio of the proposed technique is significantly higher than that of the existing major algorithms. The waveforms and spectrograms of the inverse-filtered and fully-processed signals indicate the superiority of the proposed algorithms. Assessment of the overall quality of the processed speech signals by automatic speech recognition and perceptual evaluation of speech quality test also confirms that in most cases the proposed technique yields higher scores and in the cases that it does not do so, the difference is not as significant as the other aspects of the performance evaluation. Finally, the robustness of the proposed algorithms against the background noise is investigated and compared to that of the benchmark algorithms, which shows that the proposed algorithms are capable of maintaining a rather stable performance for contaminated speech signals with SNR levels as low as 0 dB

Uncertainties of reverberation time estimation via adaptively identified room impulse responses

© 2016 Acoustical Society of America. This paper investigates the reverberation time estimation methods which employ backward integration of adaptively identified room impulse responses (RIRs). Two kinds of conditions are considered; the first is the "ideal condition" where the anechoic and reverberant signals are both known a priori so that the RIRs can be identified using system identification methods. The second is that only the reverberant speech signal is available, and blind identification of the RIRs via dereverberation is employed for reverberation time estimation. Results show that under the "ideal condition," the average relative errors in 7 octave bands are less than 2% for white noise and 15% for speech, respectively, when both the anechoic and reverberant signals are available. In contrast, under the second condition, the average relative errors of the blindly identified RIR-based reverberation time estimation are around 20%-30% except the 63 Hz octave band. The fluctuation of reverberation times estimated under the second condition is more severe than that under the ideal condition and the relative error for low frequency octave bands is larger than that for high octave bands under both conditions

OBJECTIVE AND SUBJECTIVE EVALUATION OF DEREVERBERATION ALGORITHMS

Reverberation significantly impacts the quality and intelligibility of speech. Several dereverberation algorithms have been proposed in the literature to combat this problem. A majority of these algorithms utilize a single channel and are developed for monaural applications, and as such do not preserve the cues necessary for sound localization. This thesis describes a blind two-channel dereverberation technique that improves the quality of speech corrupted by reverberation while preserving cues that affect localization. The method is based by combining a short term (2ms) and long term (20ms) weighting function of the linear prediction (LP) residual of the input signal. The developed and other dereverberation algorithms are evaluated objectively and subjectively in terms of sound quality and localization accuracy. The binaural adaptation provides a significant increase in sound quality while removing the loss in localization ability found in the bilateral implementation

Reverberation time estimation on the ACE corpus using the SDD method

Reverberation Time (T60) is an important measure for characterizing the properties of a room. The author's T60 estimation algorithm was previously tested on simulated data where the noise is artificially added to the speech after convolution with a impulse responses simulated using the image method. We test the algorithm on speech convolved with real recorded impulse responses and noise from the same rooms from the Acoustic Characterization of Environments (ACE) corpus and achieve results comparable results to those using simulated data.Comment: In Proceedings of the ACE Challenge Workshop - a satellite event of IEEE-WASPAA 2015 (arXiv:1510.00383

Reverberation: models, estimation and application

The use of reverberation models is required in many applications such as acoustic measurements, speech dereverberation and robust automatic speech recognition. The aim of this thesis is to investigate different models and propose a perceptually-relevant reverberation model with suitable parameter estimation techniques for different applications. Reverberation can be modelled in both the time and frequency domain. The model parameters give direct information of both physical and perceptual characteristics. These characteristics create a multidimensional parameter space of reverberation, which can be to a large extent captured by a time-frequency domain model. In this thesis, the relationship between physical and perceptual model parameters will be discussed. In the first application, an intrusive technique is proposed to measure the reverberation or reverberance, perception of reverberation and the colouration. The room decay rate parameter is of particular interest. In practical applications, a blind estimate of the decay rate of acoustic energy in a room is required. A statistical model for the distribution of the decay rate of the reverberant signal named the eagleMax distribution is proposed. The eagleMax distribution describes the reverberant speech decay rates as a random variable that is the maximum of the room decay rates and anechoic speech decay rates. Three methods were developed to estimate the mean room decay rate from the eagleMax distributions alone. The estimated room decay rates form a reverberation model that will be discussed in the context of room acoustic measurements, speech dereverberation and robust automatic speech recognition individually

An investigation into glottal waveform based speech coding

Coding of voiced speech by extraction of the glottal waveform has shown promise in improving the efficiency of speech coding systems. This thesis describes an investigation into the performance of such a system. The effect of reverberation on the radiation impedance at the lips is shown to be negligible under normal conditions. Also, the accuracy of the Image Method for adding artificial reverberation to anechoic speech recordings is established. A new algorithm, Pre-emphasised Maximum Likelihood Epoch Detection (PMLED), for Glottal Closure Instant detection is proposed. The algorithm is tested on natural speech and is shown to be both accurate and robust. Two techniques for giottai waveform estimation, Closed Phase Inverse Filtering (CPIF) and Iterative Adaptive Inverse Filtering (IAIF), are compared. In tandem with an LF model fitting procedure, both techniques display a high degree of accuracy However, IAIF is found to be slightly more robust. Based on these results, a Glottal Excited Linear Predictive (GELP) coding system for voiced speech is proposed and tested. Using a differential LF parameter quantisation scheme, the system achieves speech quality similar to that of U S Federal Standard 1016 CELP at a lower mean bit rate while incurring no extra delay

Glottal-synchronous speech processing

Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity of voiced speech is exploited. Traditionally, speech processing involves segmenting and processing short speech frames of predefined length; this may fail to exploit the inherent periodic structure of voiced speech which glottal-synchronous speech frames have the potential to harness. Glottal-synchronous frames are often derived from the glottal closure instants (GCIs) and glottal opening instants (GOIs). The SIGMA algorithm was developed for the detection of GCIs and GOIs from the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and GOI detection from speech signals, the YAGA algorithm provides a measured accuracy of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to reverberation than single-channel algorithms. The GCIs are applied to real-world applications including speech dereverberation, where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance of voicing detection in glottal-synchronous algorithms is demonstrated by subjective testing. The GCIs are further exploited in a new area of data-driven speech modelling, providing new insights into speech production and a set of tools to aid deployment into real-world applications. The technique is shown to be applicable in areas of speech coding, identification and artificial bandwidth extension of telephone speec