Download Sound Capture and Processing: Practical Approaches by Ivan Jelev Tashev PDF

By Ivan Jelev Tashev

Provides state of the art algorithms for sound seize, processing and enhancement

Sound catch and Processing: sensible Approaches covers the electronic sign processing algorithms and units for shooting sounds, commonly human speech. It explores the units and applied sciences used to seize, improve and technique sound for the wishes of communique and speech acceptance in glossy desktops and conversation units. This booklet supplies a finished advent to simple acoustics and microphones, with assurance of algorithms for noise relief, acoustic echo cancellation, dereverberation and microphone arrays; charting the growth of such applied sciences from their evolution to offer day commonplace.

Sound seize and Processing: useful Approaches

  • Brings jointly the cutting-edge algorithms for sound catch, processing and enhancement in a single simply obtainable quantity
  • Provides priceless implementation recommendations required to technique algorithms for actual existence purposes and units
  • Covers a few complicated sound processing options, similar to multichannel acoustic echo cancellation, dereverberation and resource separation
  • Generously illustrated with figures and charts to illustrate how sound trap and audio processing structures paintings
  • An accompanying web site containing Matlab code to demonstrate the algorithms

This precious consultant will offer audio, R&D and software program engineers within the of establishing platforms or computing device peripherals for speech enhancement with a complete evaluation of the applied sciences, units and algorithms required for contemporary desktops and communique units. Graduate scholars learning electric engineering and laptop technological know-how, and researchers in multimedia, cell-phones, interactive structures and acousticians also will take advantage of this book.Content:
Chapter 1 creation (pages 1–5):
Chapter 2 fundamentals (pages 7–53):
Chapter three Sound and Sound shooting units (pages 55–93):
Chapter four Single?Channel Noise relief (pages 95–164):
Chapter five Sound catch with Microphone Arrays (pages 165–262):
Chapter 6 Sound resource Localization and monitoring with Microphone Arrays (pages 263–305):
Chapter 7 Acoustic Echo?Reduction structures (pages 307–339):
Chapter eight De?Reverberation (pages 341–357):

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Extra resources for Sound Capture and Processing: Practical Approaches

Example text

Note that in this case we do not target signal reconstruction. 33. Generate 1024 points of a synthetic sinusoidal signal with a frequency of 1000 Hz and a sampling rate of 16 000 Hz. . Compute the spectrum of the signal in 1024 points and compare the result with the FFT function in MATLAB. Compute the signal spectrum in 8196 points. Convert the magnitudes to decibels and plot. 14. Compute the signal spectrum in 1024 points, convert it back to the time domain in 2048 points. Compare with analytically computed points.

In software implementation this is a function with an array of K numbers as the argument, which returns an array of M numbers. For practically all applications today K ¼ M, and the FFT input is an array of real numbers (real signals) and the output is an array of complex numbers (complex spectrum) with the same length. For the inverse transformation, denoted as iFFT, the input is a complex array and the output is a real array. If we have 512 samples of an audio signal, sampled at FS ¼ 16 000 Hz, then the resolution in the frequency domain will be n ¼ Fs =K ¼ 31:5 Hz.

Estimate the speech signal approximate bandwidth. 3 Temporal Properties The temporal properties of the speech signal are important and for some segments this is the only way to distinguish them from noise. 5 seconds. The speech signal cannot change faster Sound Capture and Processing 18 since it is a result of moving the muscles around the vocal tract. If there are segments where the signal changes faster, they do not belong to the speech signal. 5 seconds (excluding singing and humming). If there is a signal with spectral content changing slower than this, it is most probably not part of the speech signal either.

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