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In this work, the possibilities of an acoustic field analysis in small microphone arrays are investigated. With the increased use of mobile communication devices, such as smartphones and hearing aids, and the increase in the number of microphones in such devices, multi-channel signal processing has gained popularity. Apart from the definite signal processing, this thesis evaluates what information on the acoustic sound field and environment can be gained from the signal of such small microphone arrays. For this purpose, an innovative sound field classification was developed that determines the energies of the single sound field components. The method is based on spatial coherences of two or more acoustical. The method was successfully verified with a set of simulated and measured input signals. An adaptive automatic sensor mismatch compensation was created, which proved able to fully compensate any slow sensor drift after an initial training. Further, a new method for the blind estimation of the reverberation time based on the dependency of the coherence estimate on the evaluation parameters was proposed. The method determines the reverberation time of a room from the spatial coherence between two or more acoustic sensors.
This is the first book to provide a single complete reference on microphone arrays. Top researchers in this field contributed articles documenting the current state of the art in microphone array research, development and technological application.
Human sound localization helps to pay attention to spatially separated speakers using interaural level and time differences as well as angle-dependent monaural spectral cues. In a monophonic teleconference, for instance, it is much more difficult to distinguish between different speakers due to missing binaural cues. Spatial positioning of the speakers by means of binaural reproduction methods using head-related transfer functions (HRTFs) enhances speech comprehension. These HRTFs are influenced by the torso, head and ear geometry as they describe the propagation path of the sound from a source to the ear canal entrance. Through this geometry-dependency, the HRTF is directional and subject-dependent. To enable a sufficient reproduction, individual HRTFs should be used. However, it is tremendously difficult to measure these HRTFs. For this reason this thesis proposes approaches to adapt the HRTFs applying individual anthropometric dimensions of a user. Since localization at low frequencies is mainly influenced by the interaural time difference, two models to adapt this difference are developed and compared with existing models. Furthermore, two approaches to adapt the spectral cues at higher frequencies are studied, improved and compared. Although the localization performance with individualized HRTFs is slightly worse than with individual HRTFs, it is nevertheless still better than with non-individual HRTFs, taking into account the measurement effort.
Starting from physical theory, this work develops a novel framework for the acoustic simulation of sound radiation by loudspeakers and sound reinforcement systems. First, a theoretical foundation is derived for the accurate description of simple and multi-way loudspeakers using an advanced point-source ''CDPS'' model that incorporates phase data. The model's practical implementation is presented including measurement requirements and the GLL loudspeaker data format specification. In the second part, larger systems are analyzed such as line arrays where the receiver may be located in the near field of the source. It is shown that any extended line source can be modeled accurately after decomposition into smaller CDPS elements. The influence of production variation among elements of an array is investigated and shown to be small. The last part of this work deals with the consequences of fluctuating environmental conditions such as wind and temperature on the coherence of sound signals from multiple sources. A new theoretical model is developed that allows predicting the smooth transition from amplitude to power summation as a function of the statistical properties of the environmental parameters. A part of this work was distinguished with the AES Publications Award 2010. Parts of the proposed data format have been incorporated into the international AES56 standard.
Structure-borne sound sources are vibrational sources connected in some way to the building structure. The mechanical excitation of the building structure leads to sound radiation. This is an important source of annoyance in modern light-weight buildings. The prediction of the sound pressure level from structure-borne sound sources is highly complicated because of the complexity involved in the coupling between source and receiver structure. The current standard on characterisation of service equipment in buildings EN 12354-5, can deal with sources on heavy structures (high-mobility source) but to date, there is no engineering method available for the case of coupling between source and receiver. A case study of a washing machine on a wooden joist floor is investigated in this thesis. In the first part, measurements in the coupled state are conducted. It is shown that the normal components are sufficient to predict the sound pressure level. However, this only applies to the coupled state. In the second part, a true prediction is calculated from independently measured source and receiver quantities. The difference between predicted and directly measured sound pressure level leads to considerable errors of up to 20 dB at low frequencies. This shows that the normal components are not sufficient to predict the coupling between a washing machine and a wooden floor.
This book constitutes the refereed proceedings of the 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, held in Funchal, Madeira, Portugal, in June 2013. The 105 papers (37 oral and 68 poster ones) presented were carefully reviewed and selected from 181 submissions. The papers are organized in topical sections on computer vision, pattern recognition, image and signal, applications.
The book describes recent developments in aeroacoustic measurements in wind tunnels and the interpretation of the resulting data. The reader will find the latest measurement techniques described along with examples of the results.
Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments provides a detailed discussion of deep learning-based robust speech processing and its applications. The book begins by looking at the basics of deep learning and common deep network models, followed by front-end algorithms for deep learning-based speech denoising, speech detection, single-channel speech enhancement multi-channel speech enhancement, multi-speaker speech separation, and the applications of deep learning-based speech denoising in speaker verification and speech recognition. - Provides a comprehensive introduction to the development of deep learning-based robust speech processing - Covers speech detection, speech enhancement, dereverberation, multi-speaker speech separation, robust speaker verification, and robust speech recognition - Focuses on a historical overview and then covers methods that demonstrate outstanding performance in practical applications
158 2. Wiener Filtering 159 3. Speech Enhancement by Short-Time Spectral Modification 3. 1 Short-Time Fourier Analysis and Synthesis 159 160 3. 2 Short-Time Wiener Filter 161 3. 3 Power Subtraction 3. 4 Magnitude Subtraction 162 3. 5 Parametric Wiener Filtering 163 164 3. 6 Review and Discussion Averaging Techniques for Envelope Estimation 169 4. 169 4. 1 Moving Average 170 4. 2 Single-Pole Recursion 170 4. 3 Two-Sided Single-Pole Recursion 4. 4 Nonlinear Data Processing 171 5. Example Implementation 172 5. 1 Subband Filter Bank Architecture 172 173 5. 2 A-Posteriori-SNR Voice Activity Detector 5. 3 Example 175 6. Conclusion 175 Part IV Microphone Arrays 10 Superdirectional Microphone Arrays 181 Gary W. Elko 1. Introduction 181 2. Differential Microphone Arrays 182 3. Array Directional Gain 192 4. Optimal Arrays for Spherically Isotropic Fields 193 4. 1 Maximum Gain for Omnidirectional Microphones 193 4. 2 Maximum Directivity Index for Differential Microphones 195 4. 3 Maximimum Front-to-Back Ratio 197 4. 4 Minimum Peak Directional Response 200 4. 5 Beamwidth 201 5. Design Examples 201 5. 1 First-Order Designs 202 5. 2 Second-Order Designs 207 5. 3 Third-Order Designs 216 5. 4 Higher-Order designs 221 6. Optimal Arrays for Cylindrically Isotropic Fields 222 6. 1 Maximum Gain for Omnidirectional Microphones 222 6. 2 Optimal Weights for Maximum Directional Gain 224 6. 3 Solution for Optimal Weights for Maximum Front-to-Back Ratio for Cylindrical Noise 225 7. Sensitivity to Microphone Mismatch and Noise 230 8.
This book presents the signal processing algorithms that have been developed to process the signals acquired by a spherical microphone array. Spherical microphone arrays can be used to capture the sound field in three dimensions and have received significant interest from researchers and audio engineers. Algorithms for spherical array processing are different to corresponding algorithms already known in the literature of linear and planar arrays because the spherical geometry can be exploited to great beneficial effect. The authors aim to advance the field of spherical array processing by helping those new to the field to study it efficiently and from a single source, as well as by offering a way for more experienced researchers and engineers to consolidate their understanding, adding either or both of breadth and depth. The level of the presentation corresponds to graduate studies at MSc and PhD level. This book begins with a presentation of some of the essential mathematical and physical theory relevant to spherical microphone arrays, and of an acoustic impulse response simulation method, which can be used to comprehensively evaluate spherical array processing algorithms in reverberant environments. The chapter on acoustic parameter estimation describes the way in which useful descriptions of acoustic scenes can be parameterized, and the signal processing algorithms that can be used to estimate the parameter values using spherical microphone arrays. Subsequent chapters exploit these parameters including in particular measures of direction-of-arrival and of diffuseness of a sound field. The array processing algorithms are then classified into two main classes, each described in a separate chapter. These are signal-dependent and signal-independent beamforming algorithms. Although signal-dependent beamforming algorithms are in theory able to provide better performance compared to the signal-independent algorithms, they are currently rarely used in practice. The main reason for this is that the statistical information required by these algorithms is difficult to estimate. In a subsequent chapter it is shown how the estimated acoustic parameters can be used in the design of signal-dependent beamforming algorithms. This final step closes, at least in part, the gap between theory and practice.