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

Speech Enhancement Using Wavelet Coefficients Masking with Local Binary Patterns

In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario

Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system

The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios

Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models

In this paper, we describe how to efficiently implement an acoustic room simulator to generate large-scale simulated data for training deep neural networks. Even though Google Room Simulator in [1] was shown to be quite effective in reducing the Word Error Rates (WERs) for far-field applications by generating simulated far-field training sets, it requires a very large number of Fast Fourier Transforms (FFTs) of large size. Room Simulator in [1] used approximately 80 percent of Central Processing Unit (CPU) usage in our CPU + Graphics Processing Unit (GPU) training architecture [2]. In this work, we implement an efficient OverLap Addition (OLA) based filtering using the open-source FFTW3 library. Further, we investigate the effects of the Room Impulse Response (RIR) lengths. Experimentally, we conclude that we can cut the tail portions of RIRs whose power is less than 20 dB below the maximum power without sacrificing the speech recognition accuracy. However, we observe that cutting RIR tail more than this threshold harms the speech recognition accuracy for rerecorded test sets. Using these approaches, we were able to reduce CPU usage for the room simulator portion down to 9.69 percent in CPU/GPU training architecture. Profiling result shows that we obtain 22.4 times speed-up on a single machine and 37.3 times speed up on Google's distributed training infrastructure.Comment: Published at INTERSPEECH 2018. (https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2566.html

Auditory-based processing of communication sounds

This thesis examines the possible benefits of adapting a biologically-inspired model of human auditory processing as part of a machine-hearing system. Features were generated by an auditory model, and used as input to machine learning systems to determine the content of the sound. Features were generated using the auditory image model (AIM) and were used for speech recognition and audio search. AIM comprises processing to simulate the human cochlea, and a ‘strobed temporal integration’ process which generates a stabilised auditory image (SAI) from the input sound. The communication sounds which are produced by humans, other animals, and many musical instruments take the form of a pulse-resonance signal: pulses excite resonances in the body, and the resonance following each pulse contains information both about the type of object producing the sound and its size. In the case of humans, vocal tract length (VTL) determines the size properties of the resonance. In the speech recognition experiments, an auditory filterbank was combined with a Gaussian fitting procedure to produce features which are invariant to changes in speaker VTL. These features were compared against standard mel-frequency cepstral coefficients (MFCCs) in a size-invariant syllable recognition task. The VTL-invariant representation was found to produce better results than MFCCs when the system was trained on syllables from simulated talkers of one range of VTLs and tested on those from simulated talkers with a different range of VTLs. The image stabilisation process of strobed temporal integration was analysed. Based on the properties of the auditory filterbank being used, theoretical constraints were placed on the properties of the dynamic thresholding function used to perform strobe detection. These constraints were used to specify a simple, yet robust, strobe detection algorithm. The syllable recognition system described above was then extended to produce features from profiles of the SAI and tested with the same syllable database as before. For clean speech, performance of the features was comparable to that of those generated from the filterbank output. However when pink noise was added to the stimuli, performance dropped more slowly as a function of signal-to-noise ratio when using the SAI-based AIM features, than when using either the filterbank-based features or the MFCCs, demonstrating the noise-robustness properties of the SAI representation. The properties of the auditory filterbank in AIM were also analysed. Three models of the cochlea were considered: the static gammatone filterbank, dynamic compressive gammachirp (dcGC) and the pole-zero filter cascade (PZFC). The dcGC and gammatone are standard filterbank models, whereas the PZFC is a filter cascade, which more accurately models signal propagation in the cochlea. However, while the architecture of the filterbanks is different, they have all been successfully fitted to psychophysical masking data from humans. The abilities of the filterbanks to measure pitch strength were assessed, using stimuli which evoke a weak pitch percept in humans, in order to ascertain whether there is any benefit in the use of the more computationally efficient PZFC. Finally, a complete sound effects search system using auditory features was constructed in collaboration with Google research. Features were computed from the SAI by sampling the SAI space with boxes of different scales. Vector quantization (VQ) was used to convert this multi-scale representation to a sparse code. The ‘passive-aggressive model for image retrieval’ (PAMIR) was used to learn the relationships between dictionary words and these auditory codewords. These auditory sparse codes were compared against sparse codes generated from MFCCs, and the best performance was found when using the auditory features