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Aliasing01:18

Aliasing

449
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
449

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ALTIS: A new algorithm for adaptive long-term SNR estimation in multi-talker babble.

Roozbeh Soleymani1,2, Ivan W Selesnick1, David M Landsberger2

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This study presents a novel algorithm for real-time estimation of long-term signal-to-noise ratio (SNR) in noisy speech. The method adapts to changing conditions, outperforming existing techniques in multi-talker babble environments.

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Area of Science:

  • Speech Processing
  • Signal Processing
  • Acoustics

Background:

  • Estimating long-term signal-to-noise ratio (SNR) is crucial for real-time speech processing.
  • Existing methods struggle with dynamic multi-talker babble noise, leading to inaccurate SNR estimations.

Purpose of the Study:

  • To develop a real-time algorithm for accurate long-term SNR estimation in multi-talker babble noise.
  • To create a system robust to short-term fluctuations but responsive to long-term SNR changes.

Main Methods:

  • A novel algorithm averaging "speech-likeness" over adaptive-length long-frames.
  • Integration of an event detector to identify non-transient SNR changes.
  • Calibration for insensitivity to short-term fluctuations and transient noise.

Main Results:

  • The algorithm provides adaptive long-term SNR estimation in dynamic noisy situations.
  • Outperforms existing overall SNR estimation methods in multi-talker babble.
  • Demonstrates robustness across various numbers of talkers and SNRs.

Conclusions:

  • The proposed algorithm is effective for real-time long-term SNR estimation.
  • Its adaptive nature and performance make it suitable for real-time speech processing applications.
  • Offers a significant improvement over conventional SNR estimation techniques.