Prof. Hanna Bogucka, head of the Department of Wireless Communications at the Poznań University of Technology, discusses unnecessary inhibitions, the usefulness of microphones, and the links between people and technology.

This paper presents and compares microphone calibration methods for the simultaneous calibration of small electret microphones in a wave guide. The microphones are simultaneously calibrated to a reference microphone both in amplitude and phase. The calibration procedure is formulated on the basis of the damped plane wave propagation equation, from which the acoustics field along the wave guide is predicted, using several reference measurements. Different calibration models are presented and the methods were found to be sensitive to the formulation, as well as to the number of free parameters used during the reconstruction of the wave-field. The wave guide model based on five free parameters was found to be the preferred method for this type of calibration procedure.

Microphone array with minimum variance (MVDR) beamformer is a commonly used method for ambient noise suppression. Unfortunately, the performance of the MVDR beamformer is poor in a real reverberant room due to multipath wave propagation. To overcome this problem, we propose three improvements. Firstly, we propose end-fire microphone array that has been shown to have a better directivity index than the corresponding broadside microphone array. Secondly, we propose the use of unidirectional microphones instead of omnidirectional ones. Thirdly, we propose an adaptation of its adaptive algorithm during the pause of speech, which improves its robustness against the room reverberation and deviation from the optimal receiving direction. The performance of the proposed microphone array was theoretically analyzed using a diffuse noise model. Simulation analysis was performed for combined diffuse and coherent noise using the image model of the reverberant room. Real room tests were conducted using a four-microphone array placed in a small office room. The theoretical analysis and the real room tests showed that the proposed solution considerably improves speech quality.

The objective of the study is to assess the hearing performance of cochlear implant users in three device microphone configurations: omni-directional, directional, and beamformer (BEAMformer two-adaptive noise reduction system), in localization and speech perception tasks in dynamically changing listening environments. Seven cochlear implant users aided with Cochlear CM-24 devices with Freedom speech processor participated in the study. For the localization test in quiet and in background noise, subjects demonstrated significant differences between different microphone settings. Confusion matrices showed that in about 70% cases cochlear implant subjects correctly localized sounds within a horizontal angle of 30-40◦ (±1◦ loudspeaker apart from signal source). However localization in noise was less accurate as shown by a large number of considerable errors in localization in the confusion matrices. Average results indicated no significant difference between three microphone configurations. For speech presented from the front 3 dB SNR improvements in speech intelligibility in three subjects can be observed for beamforming system compared to directional and omni-directional microphone settings. The benefits of using different microphone settings in cochlear implant devices in dynamically changing listening conditions depend on the particular sound environment

The development of digital microphones and loudspeakers adds new and interesting possibilities of their applications in different fields, extended from industrial, medical to consumer audio markets. One of the rapidly growing field of applications is mobile multimedia, such as mobile phones, digital cameras, laptop and desktop PCs, etc. The advances have also been made in digital audio, particularly in direct digital transduction, so it is now possible to create the all-digital audio recording and reproduction chains potentially having several advantages over existing analog systems.

Whenever the recording engineer uses stereo microphone techniques, he/she has to consider a recording angle resulting from the positioning of microphones relative to sound sources, besides other acoustic factors. The recording angle, the width of a captured acoustic scene and the properties of a particular microphone technique are closely related. We propose a decision supporting method, based on the mapping of the actual position of a sound source to its position in the reproduced acoustic scene. This research resulted in a set of localisation curves characterising four most popular stereo microphone techniques. The curves were obtained by two methods: calculation, based on appropriate engineering formulae, and experiment consisting in the recording of sources and estimation of the perceived position in listening tests. The analysis of curves brings several conclusions important in the recording practice.

This paper develops an automatic method to calculate the macrotexture depth of pavement roads, using the tire/road noise data collected by the two directional microphones mounted underneath a moving test vehicle. The directional microphones collect valid tire/road noise signal at the travel speed of 10–110 km/h, and the sampling frequency is 50 kHz. The tire/road noise signal carries significant amount of road surface information, such as macrotexture depth. Using bandpass filter, principal component analysis, speed effect elimination, Gaussian mixture model, and reversible jump Markov Chain Monte Carlo, the macrotexture depth of pavement roads can be calculated from the tire/road noise data, automatically and efficiently. Compared to the macrotexture depth results by the sand-patch method and laser profiler, the acoustic method has been successfully demonstrated in engineering applications for the accurate results of macrotexture depth with excellent repeatability, at the test vehicle’s travel speed of 10-110 km/h.

Passive noise reduction means are commonly used to reduce noise in the industry but, unfortunately, their effectiveness is poor in the low frequency range. By applying active structural acoustic control to the enclosure walls significant improvement of the insulating properties in this frequency range can be achieved. In this paper a model of double panel structure with ASAC is presented. The structure consists of two aluminium plates separated by an air gap. Two inertial magnetoelectric actuators and two piezoceramic MFC sensors were used for controlling the structure. A multichannel FxLMS algorithm with virtual error microphone technique is used as a control algorithm. The signal of a virtual error microphone is extrapolated basing on signals from MFC sensors. Performance of this actively controlled structure for tonal signals at selected frequencies is presented in the article. During the study, a double panel structure was mounted on one wall of sound insulating enclosure located in an acoustic chamber. During the measurements local and global reduction of noise test signal was investigated.

The noise of motor vehicles is one of the most important problems as regards to pollution on main roads. However, this unpleasant characteristic could be used to determine vehicle speed by external observers. Building on this idea, the present study investigates the capabilities of a microphone array system to identify the position and velocity of a vehicle travelling on a previously established route. Such linear microphone array has been formed by a reduced number of microphones working at medium frequencies as compared to industrial microphone arrays built for location purposes, and operates with a processing algorithm that ultimately identifies the noise source location and reduces the error in velocity estimation

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

The aim of this paper is to describe the process of choosing the best surround microphone technique for recording of choir with an instrumental ensemble. First, examples of multichannel microphone techniques including those used in the recording are described. Then, the assumptions and details of music recording in Radio Gdansk Studio are provided as well as the process of mixing of the multichannel recording. The extensive subjective tests were performed employing a group of sound engineers and students in order to find the most preferable recording techniques. Because the final recording is based on the mix of "direct/ambient" and "direct-sound all-around" approaches, a subjective quality evaluation was conducted and on this basis the best rated multichannel techniques were chosen. The results show that listeners might consider different factors when choosing the best rated multichannel techniques in separate tasks, as different systems were chosen in the two tests.

This paper gives a detailed electroacoustic study of a new generation of monolithic CMOS micromachined electrodynamic microphone, made with standard CMOS technology. The monolithic integration of the mechanical sensor with the electronics using a standard CMOS process is respected in the design, which presents the advantage of being inexpensive while having satisfactory performance. The MEMS microphone structure consists mainly of two planar inductors which occupy separate regions on substrate. One inductor is fixed; the other can exercise out-off plane movement. Firstly, we detail the process flow, which is used to fabricate our monolithic microphone. Subsequently, using the analogy between the three different physical domains, a detailed electro-mechanical-acoustic analogical analysis has been performed in order to model both frequency response and sensitivity of the microphone. Finally, we show that the theoretical microphone sensitivity is maximal for a constant vertical position of the diaphragm relative to the substrate, which means the distance between the outer and the inner inductor. The pressure sensitivity, which is found to be of the order of a few tens of μV/Pa, is flat within a bandwidth from 50 Hz to 5 kHz.

Research work on the design of robust multimodal speech recognition systems making use of acoustic and visual cues, extracted using the relatively noise robust alternate speech sensors is gaining interest in recent times among the speech processing research fraternity. The primary objective of this work is to study the exclusive influence of Lombard effect on the automatic recognition of the confusable syllabic consonant-vowel units of Hindi language, as a step towards building robust multimodal ASR systems in adverse environments in the context of Indian languages which are syllabic in nature. The dataset for this work comprises the confusable 145 consonant-vowel (CV) syllabic units of Hindi language recorded simultaneously using three modalities that capture the acoustic and visual speech cues, namely normal acoustic microphone (NM), throat microphone (TM) and a camera that captures the associated lip movements. The Lombard effect is induced by feeding crowd noise into the speaker’s headphone while recording. Convolutional Neural Network (CNN) models are built to categorise the CV units based on their place of articulation (POA), manner of articulation (MOA), and vowels (under clean and Lombard conditions). For validation purpose, corresponding Hidden Markov Models (HMM) are also built and tested. Unimodal Automatic Speech Recognition (ASR) systems built using each of the three speech cues from Lombard speech show a loss in recognition of MOA and vowels while POA gets a boost in all the systems due to Lombard effect. Combining the three complimentary speech cues to build bimodal and trimodal ASR systems shows that the recognition loss due to Lombard effect for MOA and vowels reduces compared to the unimodal systems, while the POA recognition is still better due to Lombard effect. A bimodal system is proposed using only alternate acoustic and visual cues which gives a better discrimination of the place and manner of articulation than even standard ASR system. Among the multimodal ASR systems studied, the proposed trimodal system based on Lombard speech gives the best recognition accuracy of 98%, 95%, and 76% for the vowels, MOA and POA, respectively, with an average improvement of 36% over the unimodal ASR systems and 9% improvement over the bimodal ASR systems.

The acoustic vector sensor (AVS) is used to measure the acoustic intensity, which gives the direction-ofarrival (DOA) of an acoustic source. However, while estimating the DOA from the measured acoustic intensity the finite microphone separation (d) in a practical AVS causes angular bias. Also, in the presence of noise there exists a trade off between the bias (strictly increasing function of d) and variance (strictly decreasing function of d) of the DOA estimate. In this paper, we propose a novel method for mitigating the angular bias caused due to finite microphone separation in an AVS. We have reduced the variance by increasing the microphone separation and then removed the bias with the proposed bias model. Our approach employs the finite element method (FEM) and curves fitting to model the angular bias in terms of microphone separations and frequency of a narrowband signal. Further, the bias correction algorithm based on the intensity spectrum has been proposed to improve the DOA estimation accuracy of a broadband signal. Simulation results demonstrate that the proposed bias correction scheme significantly reduces the angular bias and improves the root mean square angular error (RMSAE) in the presence of noise. Experiments have been performed in an acoustic full anechoic room to corroborate the effect of microphone separation on DOA estimation and the efficacy of the bias correction method.