Acoustic Array Systems: Theory, Implementation, and Application (IEEE Press)
Presents a unified framework of far-field and near-fieldarray techniques for noise source identification and sound fieldvisualization, from theory to application.
Acoustic Array Systems: Theory, Implementation, andApplication provides an overview of microphone arraytechnology with applications in noise source identification andsound field visualization. In the comprehensive treatment ofmicrophone arrays, the topics covered include an introduction tothe theory, far-field and near-field array signal processingalgorithms, practical implementations, and common applications:vehicles, computing and communications equipment, compressors,fans, and household appliances, and hands-free speech. The authorconcludes with other emerging techniques and innovativealgorithms. * Encompasses theoretical background, implementationconsiderations and application know-how * Shows how to tackle broader problems in signal processing,control, and transudcers * Covers both farfield and nearfield techniques in a balancedway * Introduces innovative algorithms including equivalent sourceimaging (NESI) and high-resolution nearfield arrays * Selected code examples available for download for readers topractice on their own * Presentation slides available for instructor use
A valuable resource for Postgraduates and researchers inacoustics, noise control engineering, audio engineering, and signalprocessing.
Why Read This Book
You will get a practical, unified treatment of microphone array theory and implementation that bridges far‑field and near‑field techniques and connects algorithms to real-world measurement and visualization. If you need to design, evaluate, or deploy acoustic arrays for noise-source identification or hands‑free speech, this book gives the theory and implementation details to move from concept to working system.
Who Will Benefit
Practicing engineers and graduate students working on microphone arrays, beamforming, sound‑field imaging, or acoustic source localization who need both theory and implementation guidance.
Level: Advanced — Prerequisites: Undergraduate DSP and signals background (Fourier analysis, linear systems), linear algebra, probability/statistics, and familiarity with basic digital filtering; MATLAB experience recommended.
Key Takeaways
- Design microphone-array geometries and understand tradeoffs between aperture, resolution, and spatial aliasing.
- Implement and compare far‑field and near‑field beamforming algorithms (delay‑and‑sum, MVDR/Capon, adaptive beamformers).
- Apply subspace methods for direction‑of‑arrival (DOA) estimation and sound‑source localization (e.g., MUSIC-type approaches).
- Perform sound‑field visualization and noise‑source identification using array imaging techniques.
- Calibrate arrays and handle practical issues (sensor mismatch, reverberation, noise, finite snapshot effects) in real systems.
- Translate algorithms into practical implementations and measurement setups (MATLAB examples and implementation notes).
Topics Covered
- 1. Introduction to Microphone Array Systems and Applications
- 2. Acoustic Fundamentals and Sensor Modeling
- 3. Array Geometries and Spatial Sampling
- 4. Far‑Field Beamforming: Delay‑and‑Sum and Classical Methods
- 5. Adaptive Beamforming and MVDR/Capon Techniques
- 6. Near‑Field Array Processing and Near‑Field Imaging
- 7. Subspace Methods and High‑Resolution DOA Estimation (MUSIC, ESPRIT)
- 8. Sound‑Field Visualization and Acoustic Imaging
- 9. Array Calibration, Sensor Errors, and Robustness
- 10. Implementation Issues: Hardware, Data Acquisition, and Software
- 11. Practical Applications: Noise Source Identification and Hands‑Free Speech
- 12. Emerging Algorithms and Innovative Techniques
- Appendices: Mathematical Tools and MATLAB Implementation Notes
Languages, Platforms & Tools
How It Compares
Covers practical microphone‑array implementation like Brandstein & Ward's 'Microphone Arrays' but places more emphasis on a unified near‑field/far‑field framework and implementation; less theoretical depth than Van Trees' classical array/estimation texts.
