A Novel Robust Mel-Energy Based Voice Activity Detector for Nonstationary Noise and Its Application for Speech Waveform Compression

The voice activity detection (VAD) is crucial in all kinds of speech applications. However, almost all existing VAD algorithms suffer from the nonstationarity of both speech and noise. To combat this difficulty, we propose a new voice activity detector, which is based on the Mel-energy features and an adaptive threshold related to the signal-to-noise ratio (SNR) estimates. In this thesis, we first justify the robustness of the Bayes classifier using the Mel-energy features over that using the Fourier spectral features in various noise environments. Then, we design an algorithm using the dynamic Mel-energy estimator and the adaptive threshold which depends on the SNR estimates. In addition, a realignment scheme is incorporated to correct the sparse-and-spurious noise estimates. Numerous simulations are carried out to evaluate the performance of our proposed VAD method and the comparisons are made with a couple existing representative schemes, namely the VAD using the likelihood ratio test with Fourier spectral energy features and that based on the enhanced time-frequency parameters. Three types of noise, namely white noise (stationary), babble noise (nonstationary) and vehicular noise (nonstationary) were artificially added by the computer for our experiments. As a result, our proposed VAD algorithm significantly outperforms other existing methods as illustrated by the corresponding receiver operating curves (ROCs). Finally, we demonstrate one of the major applications, namely speech waveform compression, associated with our new robust VAD scheme and quantify the effectiveness in terms of compression efficiency

Suppression of acoustic noise in speech using spectral subtraction

technical reportA stand alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital waveform. Spectral subtraction offers a computationally efficient, processor independent, approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise for speech by subtracting the spectral noise bias calculated during non-speech activity. Secondary procedures and then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a preprocessor to narrow band voice communications systems, speech recognition systems or speaker authentication systems

The Effect of a Voice Activity Detector on the Speech Enhancement

A multimicrophone speech enhancement algorithm for binaural hearing aids that preserves interaural time delays was proposed recently. The algorithm is based on multichannel Wiener filtering and relies on a voice activity detector (VAD) for estimation of second-order statistics. Here, the effect of a VAD on the speech enhancement of this algorithm was evaluated using an envelope-based VAD, and the performance was compared to that achieved using an ideal error-free VAD. The performance was considered for stationary directional noise and nonstationary diffuse noise interferers at input SNRs from 10 to +5dB. Intelligibility-weighted SNR improvements of about 20dB and 6dB were found for the directional and diffuse noise, respectively. No large degradations (<1dB) due to the use of envelope-based VAD were found down to an input SNR of 0dB for the directional noise and 5dB for the diffuse noise. At lower input SNRs, the improvement decreased gradually to 15dB for the directional noise and 3dB for the diffuse noise.12 page(s

A New Voice Controlled Noise Cancellation Approach

This paper presents a new approach to control the operation of adaptive noise cancellers (ANCs). The technique is based on using the residual output from the noise canceller to control the decision made by a voice activity detector (VAD). Threshold of full band energy feature is adjusted according to the residual output of the noise canceller. In variable background noise environment, the threshold controlled VAD prohibits the reference input from containing some components of actual speech signal during adaptation periods. The convergence behavior of the adaptive filter is greatly improved, since the reference input will be highly correlated with the primary input. In addition, the computation power will be reduced since the output of the adaptive filter will be calculated only during non- speech periods. The threshold controlled noise canceller achieves a cleaner output in about 50% of the time required by a non-controlled noise canceller

Application of the saber method for improved spectral analysis of noisy speech

technical reportA stand alone noise suppression algorithm is described for reducing the spectral effects of acoustically added noise in speech. A fundamental result is developed which shows that the spectral magnitude of speech plus noise can be effectively approximated as the sum of magnitudes of speech and noise. Using this simple phase independent additive model, the noise bias present in the short time spectrum is reduced by subtracting off the expected noise spectrum calculated during nonspeech activity. After bias removal, the time waveform is recalculated from the modified magnitude and saved phase. This Spectral Averaging for Bias Estimation and Removal, or SABER method requires only one FFT per time window for analysis and synthesis

A Robust Noise Spectral Estimation Algorithm for Speech Enhancement in Voice Devices

In this thesis, a new robust noise spectral estimation algorithm is proposed for the purpose of single-microphone speech enhancement. This algorithm can generate the optimal noise spectral estimates in the Minimum Mean Square Error (MMSE) sense based on the speech statistics in the noisy environments. Compared to the well-adopted conventional noise spectral estimation method using the single-pole recursion, our proposed scheme is more reliable since the recursion coefficients are adaptable and optimal in the MMSE therein. We also propose a new accurate Resulting Signal-to-Noise Ratio (R-SNR) estimator as a quality measure to benchmark the existing noise spectral estimation techniques. This new R-SNR estimator can be applied to quantify not only the residual noise but also the speech distortion and therefore it can well serve as the overall speech quality measure after the noise suppression. We conduct the experiments to evaluate the performance of the noise suppression using our robust noise spectral estimation algorithm and compare it with those of two major existing noise spectral estimation methods. Through numerous simulations, we have shown that our noise suppression technique significantly outperforms the conventional methods in both stationary and nonstationary noise environments

A Computation Efficient Voice Activity Detector for Low Signal-to-Noise Ratio in Hearing Aids

This paper proposes a spectral entropy-based voice activity detection method, which is computationally efficient for hearing aids. The method is highly accurate at low SNR levels by using the spectral entropy which is more robust against changes of the noise power. Compared with the traditional fast Fourier transform based spectral entropy approaches, the proposed method of calculating the spectral entropy using the outputs of a hearing aid filter-bank significantly reduces the computational complexity. The performance of the proposed method was evaluated and compared with two other computationally efficient methods. At negative SNR levels, the proposed method has an accuracy of more than 5% higher than the power-based method with the number of floating-point operations only about 1/100 of that of the statistical model based method

Methods of Optimizing Speech Enhancement for Hearing Applications

Speech intelligibility in hearing applications suffers from background noise. One of the most effective solutions is to develop speech enhancement algorithms based on the biological traits of the auditory system. In humans, the medial olivocochlear (MOC) reflex, which is an auditory neural feedback loop, increases signal-in-noise detection by suppressing cochlear response to noise. The time constant is one of the key attributes of the MOC reflex as it regulates the variation of suppression over time. Different time constants have been measured in nonhuman mammalian and human auditory systems. Physiological studies reported that the time constant of nonhuman mammalian MOC reflex varies with the properties (e.g. frequency, bandwidth) changes of the stimulation. A human based study suggests that time constant could vary when the bandwidth of the noise is changed. Previous works have developed MOC reflex models and successfully demonstrated the benefits of simulating the MOC reflex for speech-in-noise recognition. However, they often used fixed time constants. The effect of the different time constants on speech perception remains unclear. The main objectives of the present study are (1) to study the effect of the MOC reflex time constant on speech perception in different noise conditions; (2) to develop a speech enhancement algorithm with dynamic time constant optimization to adapt to varying noise conditions for improving speech intelligibility. The first part of this thesis studies the effect of the MOC reflex time constants on speech-in-noise perception. Conventional studies do not consider the relationship between the time constants and speech perception as it is difficult to measure the speech intelligibility changes due to varying time constants in human subjects. We use a model to investigate the relationship by incorporating Meddis’ peripheral auditory model (which includes a MOC reflex) with an automatic speech recognition (ASR) system. The effect of the MOC reflex time constant is studied by adjusting the time constant parameter of the model and testing the speech recognition accuracy of the ASR. Different time constants derived from human data are evaluated in both speech-like and non-speech like noise at the SNR levels from -10 dB to 20 dB and clean speech condition. The results show that the long time constants (≥1000 ms) provide a greater improvement of speech recognition accuracy at SNR levels≤10 dB. Maximum accuracy improvement of 40% (compared to no MOC condition) is shown in pink noise at the SNR of 10 dB. Short time constants (<1000 ms) show recognition accuracy over 5% higher than the longer ones at SNR levels ≥15 dB. The second part of the thesis develops a novel speech enhancement algorithm based on the MOC reflex with a time constant that is dynamically optimized, according to a lookup table for varying SNRs. The main contributions of this part include: (1) So far, the existing SNR estimation methods are challenged in cases of low SNR, nonstationary noise, and computational complexity. High computational complexity would increase processing delay that causes intelligibility degradation. A variance of spectral entropy (VSE) based SNR estimation method is developed as entropy based features have been shown to be more robust in the cases of low SNR and nonstationary noise. The SNR is estimated according to the estimated VSE-SNR relationship functions by measuring VSE of noisy speech. Our proposed method has an accuracy of 5 dB higher than other methods especially in the babble noise with fewer talkers (2 talkers) and low SNR levels (< 0 dB), with averaging processing time only about 30% of the noise power estimation based method. The proposed SNR estimation method is further improved by implementing a nonlinear filter-bank. The compression of the nonlinear filter-bank is shown to increase the stability of the relationship functions. As a result, the accuracy is improved by up to 2 dB in all types of tested noise. (2) A modification of Meddis’ MOC reflex model with a time constant dynamically optimized against varying SNRs is developed. The model incudes simulated inner hair cell response to reduce the model complexity, and now includes the SNR estimation method. Previous MOC reflex models often have fixed time constants that do not adapt to varying noise conditions, whilst our modified MOC reflex model has a time constant dynamically optimized according to the estimated SNRs. The results show a speech recognition accuracy of 8 % higher than the model using a fixed time constant of 2000 ms in different types of noise. (3) A speech enhancement algorithm is developed based on the modified MOC reflex model and implemented in an existing hearing aid system. The performance is evaluated by measuring the objective speech intelligibility metric of processed noisy speech. In different types of noise, the proposed algorithm increases intelligibility at least 20% in comparison to unprocessed noisy speech at SNRs between 0 dB and 20 dB, and over 15 % in comparison to processed noisy speech using the original MOC based algorithm in the hearing aid