Microphone Array Signal Research Articles

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

Abstract Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

Doppler Distortion Removal in Wayside Circular Microphone Array Signals

Condition monitoring and fault diagnosis (CMFD) of key moving sources has attracted more and more attentions in recent years. Among many CMFD strategies, the wayside acoustic signal-based method can detect slight failure efficiently in time with a low-cost system. However, the nonlinear time-varying distance between the moving source and the microphone array would bring in serious Doppler distortion in the acquired signals. The Doppler distortion would result in serious amplitude distortion, frequency shift, and band extension, which is an obstacle for accurate CMFD. Considering the superior time-spatial characteristics of circular microphone array, this paper presents a short-time sparse singular value decomposition (SSVD) method based on wayside circular microphone array signals to eliminate the existed Doppler distortion. The procedure of the presented method is summarized as follows. First, the Doppler distortion array signals are decomposed into a series of array segments with a sliding window. Afterward, the corresponding moving angles (MAs) of each array segments are calculated with the introduced SSVD method. Finally, the fitting time-varying MA curve and time-domain interpolation resampling are employed to eliminate the Doppler distortion. Simulation results validated that the proposed method could achieve better localization accuracy under heavy background noise as compared to other traditional methods. Moreover, the effectiveness of the proposed method has been further verified with real experiments, which indicates excellent potential application prospects for moving vehicles, trains, planes, and so on.

Microphone array speech enhancement based on tensor filtering methods

This paper proposes a novel microphone array speech denoising scheme based on tensor filtering methods including truncated HOSVD (High-Order Singular Value Decomposition), low rank tensor approximation and multi-mode Wiener filtering. Microphone array speech signal is represented in three-order tensor space with channel, time, and spectrum modes and then tensor filtering model can be designed to process the multiway array data. As to the first method, noise can be reduced through the truncated HOSVD which is a simple scheme in tensor processing. It is more accurate to find the lower-rank approximation of the three-order tensor with Tucker model. Then MDL (Minimum Description Length) criterion is used to estimate the optimal tensor rank in the second method. Further, multimode Wiener filtering approach upon tensor analysis can be considered as the spanning of one-mode wiener filtering. How to take advantages of tensor model to obtain a set of filters is the heart of the novel scheme. The performances of the proposed three approaches are evaluated with objective indexes and listening quality test. The experimental results indicate that the proposed tenor filtering methods have potential ability of retrieving the target signal from noisy microphone array signal and the multi-mode Wiener filtering method provides the best denoising results among the three ones.

Boundary Matching Filters for Spherical Microphone and Loudspeaker Arrays

Conversion of microphone array signals into loudspeaker array signals is an essential process in high-definition spatial audio. This paper presents the theory of boundary matching filters (BMFs) for spherical array signal conversion. BMFs adapt the physical boundary conditions used during recording to the ones required for reproduction by relying on a theoretical framework provided by the Kirchhoff–Helmholtz integral equation (KHIE). Computationally, array signal conversion is performed in a transform domain where sound fields are represented in terms of spherical harmonic functions. Related research on transform-domain signal conversion filters is interpreted in the context of the KHIE. The case of a rigid recording boundary and an open reproduction boundary is addressed. The proposed rigid-to-open BMFs provide a suitable basis for designing gain-limited filters to deal with the problem of excessive gains at certain frequency bands, observed when using high-resolution arrays. Spatial, spectral, and temporal effects in sound field reconstruction when finite numbers of transducers are used in anechoic conditions are investigated analytically and exemplified numerically. Results show that the proposed gain-limited rigid-to-open BMFs outperform the existing gain-limited filters based on Tikhonov regularization because they reduce the spatial discretization effects and yield impulse responses that are more localized around their main peaks.

Perpendicular Cross-Spectra Fusion for Sound Source Localization With a Planar Microphone Array

Multiple sound source localization in reverberant environments stands as one of the most difficult challenges for many applications related to microphone array signal processing. In this paper, we describe perpendicular cross-spectra fusion (PCSF), a new direction-of-arrival (DOA) estimation algorithm, which utilizes an analytic formula for direction estimation in the time–frequency (TF) domain. Inherent to this technique is the presence of multiple direction estimation subsystems which operate in parallel, producing a multiplicity of candidate DOAs at each TF point. We define a metric of coherence, based on the property of divergence of the different DOA estimators, for assessing the reliability of different signal portions, so that only TF bins with a high quality of directional information are exploited for local DOA estimation. The resulting collection of local DOAs is provided as input to a recently proposed histogram processing approach, which is based on matching pursuit. Results based on simulation and real recordings illustrate the advantages of PCSF compared to other DOA estimation techniques subject to the same histogram-based processing, in the context of real-time multiple source localization and counting; improved performance in reverberant conditions and high tolerance to diffuse and common mode noise.

Doppler Correction Using Short-Time MUSIC and Angle Interpolation Resampling for Wayside Acoustic Defective Bearing Diagnosis

Wayside acoustic defective bearing detector (ADBD) system plays an important role in monitoring the condition of high-speed train bearings and ensuring their normal operation. However, as the system acquires acoustic signals from a passing train by a fixed microphone array, the acquired signals are severely distorted by the Doppler effect, which is an obstacle to defective bearings detection. This paper proposes a novel scheme by combining a short-time multiple signal classification (MUSIC) and angle interpolation resampling (AIR) to remove the Doppler distortion embedded in the microphone array signals. Two basic steps are included in the proposed scheme. First, the proposed short-time MUSIC method is performed on the array signals to calculate out the time-varying receiving angle information. Then, the proposed AIR method is conducted to correct the distorted signal. Compared with the traditional schemes, the proposed scheme is simple and hardly needs prior knowledge, which is expected to be applied in the ADBD system. The proposed scheme is verified by means of simulation and practical studies. Results indicate that it has superior performance to remove the Doppler distortion and has obvious advantages for the ADBD system.

Reconstruction of sound source signal by analytical passive TR in the environment with airflow

In the acoustic design of air vehicles, the time-domain signals of noise sources on the surface of air vehicles can serve as data support to reveal the noise source generation mechanism, analyze acoustic fatigue, and take measures for noise insulation and reduction. To rapidly reconstruct the time-domain sound source signals in an environment with flow, a method combining the analytical passive time reversal mirror (AP-TR) with a shear flow correction is proposed. In this method, the negative influence of flow on sound wave propagation is suppressed by the shear flow correction, obtaining the corrected acoustic propagation time delay and path. Those corrected time delay and path together with the microphone array signals are then submitted to the AP-TR, reconstructing more accurate sound source signals in the environment with airflow. As an analytical method, AP-TR offers a supplementary way in 3D space to reconstruct the signal of sound source in the environment with airflow instead of the numerical TR. Experiments on the reconstruction of the sound source signals of a pair of loud speakers are conducted in an anechoic wind tunnel with subsonic airflow to validate the effectiveness and priorities of the proposed method. Moreover the comparison by theorem and experiment result between the AP-TR and the time-domain beamforming in reconstructing the sound source signal is also discussed.

Improved algorithm of three-dimensional beamforming based on spatial cross-array

Three-dimensional beamforming based on microphone array signal processing is the expansion of traditional 2D beamforming. However, its identification accuracy is often badly reduced by the effect of grating lobes and side lobes. To overcome this problem, a method called the hybrid method beamforming (HMB) combining functional generalized inverse beamforming with multiplicative filter of spatial cross-array is proposed. In this method, the statistically optimal processing and iterated generalized inverse beamforming with regularized matrix function are utilized to obtain initial result. Then the high order function is applied to filter the output. Subsequently, a novel non-uniform spatial cross-array optimized by genetic algorithm is used to obtain sound pressure distribution. The array consists of three orthogonal sub-arrays. Mutual cancellation is realized by computing respectively with data of sub-arrays and multiplying together. With fewer microphones, the result of the improved method can be obtained with a higher spatial resolution. The proposed method is verified by the simulation and the source localization test in a room. Compared with the conventional frequency domain beamforming (FDBF) algorithm and statistically optimal array processing (SOAP) beamforming, the performance of the proposed method is significantly improved in terms of resolution of the acoustic source.

Computationally Efficient and Noise Robust DOA and Pitch Estimation

Many natural signals, such as voiced speech and some musical instruments, are approximately periodic over short intervals. These signals are often described in mathematics by the sum of sinusoids (harmonics) with frequencies that are proportional to the fundamental frequency, or pitch. In sensor (microphone) array signal processing, the periodic signals are estimated from spatio-temporal samples regarding to the direction of arrival (DOA) of the signal of interest. In this paper, we consider the problem of pitch and DOA estimation of quasi-periodic audio signals. In real-life scenarios, recorded signals are often contaminated by different types of noise, which challenges the assumption of white Gaussian noise in most state-of-the-art methods. We establish filtering methods based on noise statistics to apply to nonparametric spectral and spatial parameter estimates of the harmonics. We design minimum variance solutions with distortionless constraints to estimate the pitch from the frequency estimates, and to estimate the DOA from multichannel phase estimates of the harmonics. Applying this filtering method as the sum of weighted frequency and DOA estimates of the harmonics, we also design a joint DOA and pitch estimator. In white Gaussian noise, we derive even more computationally efficient solutions which are designed using the narrowband power spectrum of the harmonics. Numerical results reveal the performance of the estimators in colored noise compared with the Cramer–Rao lower bound. Experiments on real-life signals indicate the applicability of the methods in practical low local signal-to-noise ratios.

Signal-Dependent Spatial Filtering Based on Weighted-Orthogonal Beamformers in the Spherical Harmonic Domain

Spatial filtering with microphone arrays is a technique that can be utilized to obtain the signal of a target sound source from a specific direction. Typical approaches in the field of audio underperform in practical environments with multiple sound sources and diffuse sound. In this contribution, we propose a post-filtering technique to suppress the effect of interferers and diffuse sound. The proposed technique utilizes the cross-spectral estimates of the output of two beamformers to formulate a time-frequency soft masker. The beamformers' outputs are used only for parameter estimation and not for generating an audio signal. Two sets of beamformer weights, a constant and an adaptive, are applied to the microphone array signals for the parameter estimation. The weights of the constant beamformer are designed such that they provide a spatially narrow beam pattern that is time and frequency invariant, having a unity gain toward the direction of interest. The weights of the adaptive beamformer are formulated using linearly constrained optimization with the constraint of weighted orthogonality with respect to the constant beamformer weights, as well as the unity gain toward the look direction. The orthogonality constraint provides diffuse sound suppression while the unity gain distortionless response. The cross spectrum of these two beamformers provides the target energy at a given look direction for the post filter. The study focuses on compact microphone arrays with which the typical beamforming techniques feature a tradeoff between noise amplification and spatial selectivity, especially in the low frequency region. The proposed method is evaluated with instrumental measures and listening tests under different reverberation times, in dual and multitalker scenarios. The evaluation shows that the proposed method provides a better performance when compared with a previous state-of-the-art spatial filter based on cross-pattern coherence, a linearly constrained beamformer and a Wiener postfilter.

Estimation of source location and ground impedance using a hybrid multiple signal classification and Levenberg–Marquardt approach

A microphone array signal processing method for locating a stationary point source over a locally reactive ground and for estimating ground impedance is examined in detail in the present study. A non-linear least square approach using the Levenberg–Marquardt method is proposed to overcome the problem of unknown ground impedance. The multiple signal classification method (MUSIC) is used to give the initial estimation of the source location, while the technique of forward backward spatial smoothing is adopted as a pre-processer of the source localization to minimize the effects of source coherence. The accuracy and robustness of the proposed signal processing method are examined. Results show that source localization in the horizontal direction by MUSIC is satisfactory. However, source coherence reduces drastically the accuracy in estimating the source height. The further application of Levenberg–Marquardt method with the results from MUSIC as the initial inputs improves significantly the accuracy of source height estimation. The present proposed method provides effective and robust estimation of the ground surface impedance.

Nonlinear speech enhancement by virtual increase of channels and maximum SNR beamformer

In this paper, we propose a new microphone array signal processing technique, which increases the number of microphones virtually by generating extra signal channels from real microphone signals. Microphone array signal processing methods such as speech enhancement are effective for improving the quality of various speech applications such as speech recognition and voice communication systems. However, the performance of speech enhancement and other signal processing methods depends on the number of microphones. Thus, special equipment such as a multichannel A/D converter or a microphone array is needed to achieve high processing performance. Therefore, our aim was to establish a technique for improving the performance of array signal processing with a small number of microphones and, in particular, to increase the number of channels virtually by synthesizing virtual microphone signals, or extra signal channels, from two channels of microphone signals. Each virtual microphone signal is generated by interpolating a short-time Fourier transform (STFT) representation of the microphone signals. The phase and amplitude of the signal are interpolated individually. The phase is linearly interpolated on the basis of a sound propagation model, and the amplitude is nonlinearly interpolated on the basis of β divergence. We also performed speech enhancement experiments using a maximum signal-to-noise ratio (SNR) beamformer equipped with virtual microphones and evaluated the improvement in performance upon introducing virtual microphones.

AUTOMATIC MIC ADJUSTMENT USING DC MOTOR

Beamforming theory is used to measure the phase angle between two input signals. Automatic adjustment of height and position of MIC using dc motors. Horizontal and vertical DC motors are attached with the mic. Microphone array signal processing is to enhance and extract information carried by acoustic waves received from a number of microphones. Differently placed microphones are receiving more or less the same signal but with a small time shift. The time delay estimation is calculated using LabVIEW software.

On the design and implementation of linear differential microphone arrays.

Differential microphone array (DMA), a particular kind of sensor array that is responsive to the differential sound pressure field, has a broad range of applications in sound recording, noise reduction, signal separation, dereverberation, etc. Traditionally, an Nth-order DMA is formed by combining, in a linear manner, the outputs of a number of DMAs up to (including) the order of N - 1. This method, though simple and easy to implement, suffers from a number of drawbacks and practical limitations. This paper presents an approach to the design of linear DMAs. The proposed technique first transforms the microphone array signals into the short-time Fourier transform (STFT) domain and then converts the DMA beamforming design to simple linear systems to solve. It is shown that this approach is much more flexible as compared to the traditional methods in the design of different directivity patterns. Methods are also presented to deal with the white noise amplification problem that is considered to be the biggest hurdle for DMAs, particularly higher-order implementations.

Spectro-Temporal Filtering for Multichannel Speech Enhancement in Short-Time Fourier Transform Domain

In this letter, we propose a spectro-temporal filtering algorithm for multichannel speech enhancement in the short-time Fourier transform (STFT) domain. Compared with the traditional multiplicative filtering technique, the proposed method takes account of interdependencies between components in adjacent frames and frequency bins. For spectro-temporal filtering, speech and noise power spectral density (PSD) matrices are estimated based on an extended formulation utilizing temporal and spectral correlations, and the parametric noise reduction filter based on these PSD matrices is applied to the input microphone array signal. Moreover, multichannel speech presence probabilities are also estimated within a unified framework. A number of experimental results show that the proposed spectro-temporal filtering method improves the performance of multichannel speech enhancement.

Sound-Separation System using Spherical Microphone Array with Three-Dimensional Directivity—KIKIWAKE 3D: Language Game for Children

Abstract Mixed sounds can be separated from multiple sound sources using microphone array sensor and signal processing. We believe that promotion of interest in this technique can lead to significant future development in science and technology. To investigate this technique, we designed a language game for children called “KIKIWAKE 3D” that uses a sound-source-separation system to arouse children’s interest in this technology. However, the microphone array sensor in a previous research had a limited scope in separating sounds. We developed a spherical microphone array sensor with three-dimensional directivity designed for this game. In this paper, we report the evaluation of this microphone array sensor in adapting to this game by separating the sound level and using questionnaires.

Mixed-order Ambisonics encoding of cylindrical microphone array signals

Mixed-order Ambisonics encoding of cylindrical microphone array signals

Room geometry inference based on spherical microphone array eigenbeam processing

The knowledge of parameters characterizing an acoustic environment, such as the geometric information about a room, can be used to enhance the performance of several audio applications. In this paper, a novel method for three-dimensional room geometry inference based on robust and high-resolution beamforming techniques for spherical microphone arrays is presented. Unlike other approaches that are based on the measurement and processing of multiple room impulse responses, here, microphone array signal processing techniques for uncontrolled broadband acoustic signals are applied. First, the directions of arrival (DOAs) and time differences of arrival (TDOAs) of the direct signal and room reflections are estimated using high-resolution robust broadband beamforming techniques and cross-correlation analysis. In this context, the main challenges include the low reflected-signal to background-noise power ratio, the low energy of reflected signals relative to the direct signal, and their strong correlation with the direct signal and among each other. Second, the DOA and TDOA information is combined to infer the room geometry using geometric relations. The high accuracy of the proposed room geometry inference technique is confirmed by experimental evaluations based on both simulated and measured data for moderately reverberant rooms.

Real-time Sound Source Localization on Graphics Processing Units

Sound source localization is an important topic in microphone array signal processing applications, such as camera steering systems, human-machine interaction or surveillance systems. The Steered Response Power with Phase Transform (SRP- PHAT) algorithm is one of the most well-known approaches for sound source localization due to its good performance in noisy and reverberant environments. The algorithm analyzes the sound power captured by a microphone array on a grid of spatial points in a given room. While localization accuracy can be improved by using a high resolution spatial grid and a high number of microphones, performing the localization task in these circumstances requires a high computational demand. Graphics Processing Units (GPUs) are highly parallel programmable coprocessors that provide massive computation when the needed operations are properly parallelized. This paper analyzes the performance of a real-time sound source localization system whose processing is totally carried out on a GPU. The proposed implementation yields maximum parallelism by adapting the required computations to different GPU architectures (Tesla, Fermi and Kepler).

A Time Delay Estimation Method Based on Wavelet Transform and Speech Envelope for Distributed Microphone Arrays

1 Abstract—A time delay estimation method based on wavelet transform and speech envelope is proposed for distributed microphone arrays. This method first extracts the speech envelopes of the signals processed with multi-level discrete wavelet transform, and then makes use of the speech envelopes to estimate a coarse time delay. Finally it searches for the accurate time delay near the coarse time delay by the crosscorrelation function calculated in time domain. The simulation results illustrate that the proposed method can accurately estimate the time delay between two distributed microphone array signals.