T. May

Publications (2)1.5 Total impact

• Article: A Probabilistic Model for Robust Localization Based on a Binaural Auditory Front-End

  T. May, S. van de Par, A. Kohlrausch
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  ABSTRACT: Although extensive research has been done in the field of machine-based localization, the degrading effect of reverberation and the presence of multiple sources on localization performance has remained a major problem. Motivated by the ability of the human auditory system to robustly analyze complex acoustic scenes, the associated peripheral stage is used in this paper as a front-end to estimate the azimuth of sound sources based on binaural signals. One classical approach to localize an acoustic source in the horizontal plane is to estimate the interaural time difference (ITD) between both ears by searching for the maximum in the cross-correlation function. Apart from ITDs, the interaural level difference (ILD) can contribute to localization, especially at higher frequencies where the wavelength becomes smaller than the diameter of the head, leading to ambiguous ITD information. The interdependency of ITD and ILD on azimuth is a complex pattern that depends also on the room acoustics, and is therefore learned by azimuth-dependent Gaussian mixture models (GMMs). Multiconditional training is performed to take into account the variability of the binaural features which results from multiple sources and the effect of reverberation. The proposed localization model outperforms state-of-the-art localization techniques in simulated adverse acoustic conditions.
  IEEE Transactions on Audio Speech and Language Processing 02/2011; · 1.50 Impact Factor
• Article: Speaker distance detection using a single microphone

  E. Georganti, T. May, S. van de Par, Aki Harma, J. Mourjopoulos
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  ABSTRACT: A method to detect the distance of a speaker from a single microphone in a room environment is proposed. Several features, related to statistical parameters of speech source excitation signals, are introduced and are shown to depend on the distance between source and receiver. Those features are used to train a pattern recognizer for distance detection. The method is tested using a database of speech recordings in four rooms with different acoustical properties. Performance is shown to be independent of the signal gain and level, but depends on the reverberation time and the characteristics of the room. Overall, the system performs well especially for close distances and for rooms with low reverberation time and it appears to be robust to small distance mismatches. Finally, a listening test is conducted in order to compare the results of the proposed method to the performance of human listeners.
  IEEE Transactions on Acoustics Speech and Signal Processing 01/2011; 19.