Eleftheria Georganti

Sound Source Distance Estimation in Rooms based on Statistical Properties of Binaural Signals

A novel method for the estimation of the distance of a sound source from binaural speech signals is proposed. The method relies on several statistical features extracted from such signals and their binaural cues. Firstly, the standard deviation of the difference of the magnitude spectra of the left and right binaural signals is used as a feature for this method. In addition, an extended set of additional statistical features that can improve distance detection is extracted from an auditory front-end which models the peripheral processing of the human auditory system. The method incorporates the above features into two classification frameworks based on Gaussian mixture models and Support Vector Machines and the relative merits of those frameworks are evaluated. The proposed method achieves distance detection when tested in various acoustical environments and performs well in unknown environments. Its performance is also compared to an existing binaural distance detection method.

Binaural extension and performance of single-channel spectral subtraction dereverberation algorithms

Single-channel spectral subtraction algorithms are commonly used to suppress late reverberation. A binaural extension of such methods, apart from suppressing reverberation without introducing processing artifacts, should also preserve the signals binaural localization cues. Here, three state-of-the-art spectral subtraction dereverberation algorithms are extended into a binaural context utilizing three alternative bilateral gain adaptation schemes and are compared to an extension derived from a Delay and Sum Beamformer. Objective results for several experimental conditions reveal the most prominent binaural extensions.

Analysis of room transfer function and reverberant signal statistics

For some time now, statistical analysis has been a valuable tool in analyzing room transfer functions (RTFs). This work examines existing statistical time‐frequency models and techniques for RTF analysis (e.g., Schroeders stochastic model and the standard deviation over frequency bands for the RTF magnitude and phase). RTF fractional octave smoothing, as with 1/3 octave analysis, may lead to RTF simplifications that can be useful for several audio applications, like room compensation, room modeling, auralisation purposes. The aim of this work is to identify the relationship of optimal response smoothing (e.g., as in complex smoothing) with respect to the original RTF statistics. More specifically, the RTF statistics, derived after the complex smoothing calculation, are compared to the original statistics across space inside typical rooms, by varying the source, the receiver position and the corresponding ratio of the direct and reverberant signal. In addition, this work examines the statistical quantities for speech and audio signals prior to their reproduction within rooms and when recorded in rooms. Histograms and other statistical distributions are used to compare RTF minima of typical “anechoic” and “reverberant” audio speech signals, in order to model the alterations due to room acoustics. The above results are obtained from both in‐situ room response measurements and controlled acoustical response simulations.

Speaker Distance Detection Using a Single Microphone

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.

Room statistics and direct-to-reverberant ratio estimation from dual-channel signals

A method for the estimation of the direct-to-reverberant-ratio (DRR) from dual-channel microphone recordings without having knowledge of the source signal is proposed. The method is based on previous findings for the statistics of the room transfer function spectral standard deviation and its relationship to the DRR. A novel relationship for the standard deviation of the difference of the magnitude spectra of dualchannel microphone recordings as a function of the DRR is derived. The proposed DRR estimation method is tested and evaluated on reverberant signals recorded in various rooms and at various source/receiver distances.

Speech dereverberation based on a recorded handclap

A semi-blind framework for the suppression of late speech reverberation is presented. The method is based on spectral subtraction and utilizes a simple recorded handclap to estimate the Power Spectral Density (PSD) of late reverberation. A statistical analysis of measured Room Impulse Responses (RIRs) and recorded handclaps demonstrates the sufficiency of the above estimation. Dereverberation results show that the proposed technique achieves significant reverberation suppression at multiple speaker positions in each room without compromising the quality of the estimated signals.

Extracting Sound-Source-Distance Information from Binaural Signals

The problem of distance estimation by computational methods utilizing binaural information is discussed. Initially, a brief overview is given concerning findings related to the auditory distance perception. Then, several acoustical parameters that depend on the distance between the source and the receiver especially within reverberant rooms are presented. An overview of several existing distance estimation techniques using binaural signals is given and a recent distance estimation method is presented in more detail. This method relies on several statistical features extracted from binaural signals and incorporates all the above features into a classification framework based on Gaussian Mixture models.

Effects of wide dynamic range compression on speech signals with respect to reverberation

The sound perception in enclosed spaces is dominated by the room acoustics properties of the enclosures. Today, it is known that reverberation generated by walls and obstacles challenges hearing-impaired people, even with hearing aids, and several studies have been conducted to address this problem. However, relatively little is known about how various signal processing blocks (i.e., beamforming, wide dynamic range compression) within hearing aids affect the reverberation content of the speech signals. Aim of this work was to investigate and quantify the effects of wide dynamic range compression on the reverberant component of speech signals employing both subjective and objective methods. Several objective metrics which correlate with reverberation were applied on the speech signals before and after the compression. Moreover, a listening test with 14 normal hearing participants was performed to assess whether the changes in the reverberation content of the compressed signals were perceivable. The percept...

Analysis of the acoustical environment based on the signals' statistics

In this study, the results of the ongoing work of the authors on the topic of the analysis of the acoustical environment from reverberant signals will be presented. Initially, some theoretical aspects on the relationships of the statistical quantities of the room transfer functions and the reverberant signals will be given. Then, the way that these statistical relationships can assist acoustical scene analysis methods will be discussed. The advantage of the use of two-channel (i.e. binaural) instead of single channel signals will be underlined. Finally, the implementation details of methods for the estimation of various acoustical parameters from signals will be presented.