Dual-microphone Speech Enhancement Research Articles

Dual Microphone Speech Enhancement Based on Statistical Modeling of Interchannel Phase Difference

The interchannel phase difference (IPD) may be one of the most widely-used spatial cues in multichannel speech processing, and has been used in beamformers and post filters for speech enhancement. The coherence, which is also used as a feature for speech enhancement, can provide information on the reliability of the IPD for the estimation of the speech presence probability (SPP). In this paper, we propose dual microphone speech enhancement adopting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> SPP estimation based on statistical modeling of the IPD. The marginal distribution of the IPD is derived from the distribution of the relative transfer function which is parameterized with the IPD and coherence, with a single assumption that the observed discrete Fourier transform (DFT) coefficients in each frequency are distributed according to a complex bivariate Gaussian distribution. Given the direction of arrival of the desired signal, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> SPP is obtained using the IPD distributions with and without the information on the location of the interfering source, and is applied to speech enhancement. Experimental results for various types and locations of noise, signal-to-noise ratios, reverberation times, and locations of the target source showed that the proposed method outperformed previously proposed approaches utilizing IPD information.

A Real-Time Dual-Microphone Speech Enhancement Algorithm Assisted by Bone Conduction Sensor.

The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech sound quality and intelligibility improvements for all noise types and levels when compared with the AC-only beamformer with a postfiltering algorithm.

Research of front-end speech enhancement and beamforming algorithm based on dual microphoneforcochlear implant

Speech enhancement methods based on microphone array adopt many microphones to record speech signal simultaneously. As spatial information is increased, these methods can increase speech recognition for cochlear implant in noisy environment. Due to the size limitation, the number of microphones used in the cochlear implant cannot be too large, which limits the design of microphone array beamforming. To balance the size limitation of cochlear implant and the spatial orientation information of the signal acquisition, we propose a speech enhancement and beamforming algorithm based on dual thin uni-directional / omni-directional microphone pairs (TP) in this paper. Each TP microphone contains two sound tubes for signal acquisition, which increase the overall spatial orientation information. In this paper, we discuss the beamforming characteristics with different gain vectors and the influence of the inter-microphone distance on beamforming, which provides valuable theoretical analysis and engineering parameters for the application of dual microphone speech enhancement technology in cochlear implants.

FPGA based dual microphone speech enhancement

This paper proposes an efficient reconfigurable hardware design of dual microphone speech enhancement technique using sound source localization and multi band spectral subtraction methods with elimination of background noise. Firstly, we have used a time delay of arrival algorithm using phase transform (PHAT) to achieve the time difference between the microphone signals. PHAT based filter can reach high SNR gains, which makes it very suitable for localizing the sound source in a microphone array system. After adjustment of the delay between the signals, multi band spectral subtraction technique enhances the signal from the background noise environment in each of the frequency bands. Our design has been implemented in Spartan6 Lx45 FPGA and we have presented the implementation results in terms of subjective and objective evaluations. We have also compared the angular separation between the microphones to the resultant angle between the microphones and observed that proposed design provides very sharp accuracy in every corresponding angle. The objective and subjective evaluation established that our design provides better throughput compared to the existing state of the art research works. The evaluation infers that our proposed hardware induce feasibility for hand-held devices in background noisy environment.

Hearing Aid Speech Enhancement Using Phase Difference-Controlled Dual-Microphone Generalized Sidelobe Canceller

This paper proposes a new technique for improving a generalized sidelobe canceller (GSC) for dual-microphone speech enhancement to be applied in an auditory device such as a hearing aid. Here, the GSC is implemented on a 32-channel uniform polyphase discrete Fourier transform filter bank, where the overall algorithm processing delay is 8 ms to meet hearing aid requirements. The proposed method can improve the fixed beamformer (FBF) and control the adaptive algorithm in the noise canceller (NC) using the phase difference obtained from dual-microphone signals. For this, spatial cues such as the phase differences are used to estimate the target-to-non-target directional signal ratio (TNR). A target-directional speech enhancing spectral gain-attenuator is calculated based on the estimated TNR, which is then incorporated to improve the FBF in the GSC. Furthermore, the weight update of the adaptive NC in the GSC is formulated using the phase difference-based TNR. The experimental results show that the auditory speech enhancement system that employs the proposed dual-microphone GSC algorithm provides better perceptual quality and intelligibility scores than conventional methods such as a beamformer, phase-error-based filter (PEF), GSC, or PEF-controlled GSC under multiple noise conditions of signal-to-noise ratio range 0-20 dB.

Speech Enhancement Control Design Algorithm for Dual-Microphone Systems Using β-NMF in a Complex Environment

Single-microphone speech enhancement algorithms by using nonnegative matrix factorization can only utilize the temporal and spectral diversity of the received signal, making the performance of the noise suppression degrade rapidly in a complex environment. Microphone arrays have spatial selection and high signal gain, so it applies to the adverse noise conditions. In this paper, we present a new algorithm for speech enhancement based on two microphones with nonnegative matrix factorization. The interchannel characteristic of each nonnegative matrix factorization basis can be modeled by the adopted method, such as the amplitude ratios and the phase differences between channels. The results of the experiment confirm that the proposed algorithm is superior to other dual-microphone speech enhancement algorithms.

Noise dependent coherence-super Gaussian based dual microphone speech enhancement for hearing aid application using smartphone

In this work, the coherence between speech and noise signals is used to obtain a Speech Enhancement (SE) gain function, in combination with a Super-Gaussian Joint Maximum a Posteriori (SGJMAP) single microphone SE gain function. The proposed SE method is implemented on a smartphone that works as an assistive device to hearing aids in real-time. Although coherence SE gain function suppresses the background noise well, it distorts the speech. In contrary, SE using SGJMAP improves speech quality with the introduction of musical noise, which we contain by using a post filter. The weighted union of these two gain functions strikes a balance between noise suppression and speech distortion. A “weighting” parameter is introduced in the derived gain function that can be controlled by the smartphone user based on different background noises and their comfort level of hearing, which proves the practical usability of the developed SE method implemented on the smartphone. Objective and subjective measures of the proposed method show significant improvements in both quality and intelligibility compared to the state of the art SE techniques considered in this work for several noisy conditions at the signal to noise ratio levels of -5 dB, 0 dB, and 5 dB.

Coherence based dual microphone speech enhancement technique using FPGA

This paper, presents a design and implementation of dual microphone coherence based speech enhancement technique using field programmable gate array (FPGA). In order to have a proper enhancement of dual microphone system, we require to estimate the time delay of arrival (TDOA) between the two microphone signals which is followed by the application of the proposed speech enhancement algorithm. We have used TDOA algorithm based on phase transform to minimize the effect of reverberation for localization of the sound sources. Coherence based technique has been used for speech enhancement process which requires no background noise estimation. In this way, we can achieve a high localization accuracy and also the capability of dealing with coherent noise. In the proposed system, TDOA and speech enhancement processes are executed concurrently exploiting the parallel logic blocks of FPGA, thus increasing the throughput of the system to a great extent. We have implemented our design on Spartan6 Lx45 FPGA device. The subjective evaluation of the proposed design with normal hearing listeners using comprehensibility listing test has been done and its performance has been compared to the existing state of the art research works. The objective evaluation of the proposed design also designates the significant melioration over the existing state of the art research works. The subjective and objective evaluation infer that our proposed hardware induce feasible solution for hearing aid and other hand-held devices.

Speech enhancement in dual-microphone mobile phones using Kalman filter

In this paper, a dual-microphone speech enhancement algorithm for the mobile phones is proposed. The adopted method exploits the coherence function algorithm and the Kalman filter. This algorithm has a simple implementation that does not need a prediction of interfering signals statistics. In addition, this algorithm can be used in small devices with so closely distance between the two microphones. Moreover, the use of such algorithm allows reducing multiple noise sources at many azimuths positions. Finally, the new algorithm proves its performances referring to the perceptual evaluation of speech quality and the time domain waveforms.

Direction-of-Arrival Based SNR Estimation for Dual-Microphone Speech Enhancement

In this paper, we propose a method for estimating target speech by exploring the spatial cues in adverse noise environments. This method is able to reliably estimate the signal-to-noise ratio (SNR) using the phase difference obtained from dual-microphone signals. To this end, spatial cues such as the phase difference are used to estimate the target-to-non-target directional signal ratio (TNR). Based on the estimated TNR, a direction-of-arrival (DOA)-based SNR is then estimated by using a statistical model-based log-likelihood ratio test for the target speech activity decision followed by a decision-directed approach. The estimate is then incorporated into a Wiener filter in order to obtain a spectral-gain attenuator. The perceptual evaluation of speech quality shows that the performance of a dual-microphone speech enhancement system employing the proposed estimation method outperforms single- and dual-microphone speech enhancement systems that use conventional methods such as Wiener filtering, beamforming, or phase-error-based filtering under noise conditions whose SNR ranges from 0 to 20 dB.

Noise variance estimation based on dual-channel phase difference for speech enhancement

In this paper, we propose a method for estimating a signal-to-noise ratio (SNR) in order to improve the performance of a dual-microphone speech enhancement algorithm. The proposed method is able to reliably estimate both a priori and a posteriori SNRs by exploring a direction-of-arrival (DOA)-based local SNR that is defined by using spatial cues obtained from dual-microphone signals. The estimated a priori and a posteriori SNRs are then incorporated into a Wiener filter. Consequently, it is shown from an objective perceptual evaluation of speech quality (PESQ) comparison and a subjective listening test that a speech enhancement algorithm employing the proposed SNR estimate outperforms those using conventional single- or dual-microphone speech enhancement algorithms such as the Wiener filter, beamformer, or phase error-based filter under different noise conditions ranging from 0 to 20 dB.

Energy Based Dual-Microphone Electronic Speech Segregation

Speech segregation based on energy has a good performance on dual-microphone electronic speech signal processing. The implication of the binary mask to an auditory mixture has been shown to yield substantial improvements in signal-to-noise-ratio (SNR) and intelligibility. To evaluate the performance of a binary mask based dual microphone speech enhancement algorithm, various spatial noise sources and reverberation test conditions are used. Two compare dual microphone systems based on energy difference and machine learning are used at the same time. Result with SNR and speech intelligibility show that more robust performance can be achieved than the two compare systems.

A Dual-Microphone Algorithm That Can Cope With Competing-Talker Scenarios

This paper introduces a novel technique for signal-to-noise ratio (SNR) estimation for scenarios where two closely-spaced microphones are available. The proposed technique utilizes the real and imaginary parts of the coherence function between the input signals to estimate the SNR without assuming prior knowledge of the noise statistics. The corresponding dual-microphone speech enhancement algorithm utilizes a Wiener filter as a gain function constructed using the SNR values computed by the coherence function. Since the proposed SNR estimation technique does not require access to noise statistics, it can be applied in situations where interfering speakers are present. An adaptive speech reception threshold (SRT) test was used to assess the intelligibility of speech processed by the proposed algorithm in scenarios where one or two interfering talkers were present in anechoic and reverberant conditions. Intelligibility listening tests were conducted with both normal-hearing (NH) and cochlear implant (CI) listeners. Results revealed significant improvements in intelligibility and quality over a (baseline) fixed directional algorithm and a well-established beamformer algorithm. In a nearly anechoic room with competing talkers, the improvement in SRT obtained relative to the directional microphone ranged from 5-10 dB, while the improvement obtained by the beamformer was about 2 dB. In reverberant environments, the improvement in SRT remained high (4-7 dB) at T60 = 220 ms, and decreased to 1-2 dB at T60 = 465 ms. Overall, the proposed algorithm provided significant benefits in intelligibility in anechoic and mildly reverberant environments making it suitable for hearing aid and cochlear implant applications.

A Dual-Microphone Speech Enhancement Algorithm Based on the Coherence Function

A novel dual-microphone speech enhancement technique is proposed in the present paper. The technique utilizes the coherence between the target and noise signals as a criterion for noise reduction and can be generally applied to arrays with closely-spaced microphones, where noise captured by the sensors is highly correlated. The proposed algorithm is simple to implement and requires no estimation of noise statistics. In addition, it offers the capability of coping with multiple interfering sources that might be located at different azimuths. The proposed algorithm was evaluated with normal hearing listeners using intelligibility listening tests and compared against a well-established beamforming algorithm. Results indicated large gains in speech intelligibility relative to the baseline (front microphone) algorithm in both single and multiple-noise source scenarios. The proposed algorithm was found to yield substantially higher intelligibility than that obtained by the beamforming algorithm, particularly when multiple noise sources or competing talker(s) were present. Objective quality evaluation of the proposed algorithm also indicated significant quality improvement over that obtained by the beamforming algorithm. The intelligibility and quality benefits observed with the proposed coherence-based algorithm make it a viable candidate for hearing aid and cochlear implant devices.

A noise cross PSD estimator for dual-microphone speech enhancement based on minimum statistics

Some two-microphone noise reduction techniques that work in the frequency domain exploit coherence function between two noisy signals. They have shown good results when noise signals on two sensors are uncorrelated, but their performance decreases with correlated noises. Coherence based methods can be improved when the cross power spectral density (CPSD) of correlated noise signals is available. In this paper, we propose a new method for estimation of the CPSD of the noise, which is based on the minimum tracking technique. Despite the fact that the proposed estimator does not need to implement a voice activity detector (VAD), its performance is comparable to a CPSD estimator that uses an ideal VAD.

Using power level difference for near field dual-microphone speech enhancement

In this contribution, a novel dual-channel speech enhancement technique is introduced. The proposed approach uses the dissimilarity between the power of received signals in the two channels as a criterion for speech enhancement and noise reduction. We claim that in near field conditions, where the distances between microphones and sound source are short, the difference in the received power levels at the two microphones is an estimate of the clean speech signal power. Then, apply this theory to present an optimum method for speech enhancement. Fortunately, the method has the ability to cope with problems such as transient noise and nearby microphones which are two of the main problems of the proposed dual-microphone speech enhancement techniques. Using objective speech quality measures and spectrogram analysis, we show that the proposed method results in improved speech quality.

Low-Power Dual-Microphone Speech Enhancement Using Field Programmable Gate Arrays

This paper discusses two implementations of a dual-microphone phase-based speech enhancement technique. The implementations compared are based on a field-programmable gate array (FPGA) and a digital signal processor (DSP). The time-varying, frequency-dependent, phase-difference between each incoming sound signal is used to mask each respective frequency, thereby reducing noise by minimizing the contributions of signal frequency components that have a low signal-to-noise ratio (SNR). Phase-based filtering can achieve high SNR gains with just two microphones, making it ideal for small devices that lack the room for a multimicrophone array. Moreover, these devices often have a limited battery life and lack the processing power needed for software-based speech enhancement. This paper presents an FPGA-based dual-microphone speech enhancement implementation, which was designed specifically for low-power operation. This implementation is compared with an off-the-shelf DSP implementation, with respect to processing capabilities and power utilization.

Phase-Based Dual-Microphone Speech Enhancement Using A Prior Speech Model

This paper proposes a phase-based dual-microphone speech enhancement technique that utilizes a prior speech model. Recently, it has been shown that phase-based dual-microphone filters can result in significant noise reduction in low signal-to-noise ratio [(SNR) less than 10 dB] conditions and negligible distortion at high SNRs (greater than 10 dB), as long as a correct filter parameter is chosen at each SNR. While prior work utilizes a constant parameter for all SNRs, we present an SNR-adaptive filter parameter estimation algorithm that maximizes the likelihood of the enhanced speech features based on a prior speech model. Experimental results using the CARVUI database show significant speech recognition accuracy rate improvement over alternative techniques in low SNR situations (e.g., an improvement of 11% in word error rate (WER) over postfiltering and 23% over delay-and-sum beamforming at 0 dB) and negligible distortion at high SNRs. The proposed adaptive approach also significantly outperforms the original phase-based filter with a constant parameter. Furthermore, it improves the filter's robustness when there are errors in time delay estimation