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.

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.

Microphone leakage is one of the most prevalent problems in audio applications involving multiple instruments and multiple microphones. Currently, sound engineers have limited solutions available to them. In this paper, the applicability of two widely used signal enhancement methods to this problem is discussed, namely blind source separation and noise suppression. By extending previous work, it is shown that the noise suppression framework is a valid choice and can effectively address the problem of microphone leakage. Here, an extended form of the single channel Wiener filter is used which takes into account the individual audio sources to derive a multichannel noise term. A novel power spectral density (PSD) estimation method is also proposed based on the identification of dominant frequency bins by examining the microphone and output signal PSDs. The performance of the method is examined for simulated environments with various source-microphone setups and it is shown that the proposed approach efficiently suppresses leakage.

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 signal's 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.

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.

Real-time digital wireless playback of CD-quality audio in multipoint setups using quality of service enhancements is analyzed and evaluated in this work. A novel methodology is introduced for simulating wireless digital audio delivery as well as for theoretically deriving the playback distortions. This methodology allows the accurate wireless digital audio delivery and reproduction simulation and leads to significant results for both the wireless networking and audio playback performance, while it provides a framework for defining the optimal parameters for error-free wireless stereo audio reproduction

For audio and acoustic system responses, the auditory system tends to smooth high frequency spectral regions and to register, mainly, low-frequency components of late echoes in the time domain. To model such processing, a theoretical model for non-uniform spectral smoothing is introduced allowing the choice of arbitrary frequency resolution profiles to match such auditory features. This generalized framework is extended to allow mapping of such smoothing spectra into the time domain.

A novel speech enhancement technique is presented based on the definition of the psychoacoustically derived quantity of audible noise spectrum and its subsequent suppression using optimal nonlinear filtering of the short-time spectral amplitude (STSA) envelope. The filter operates with sparse spectral estimates obtained from the STSA, and, when these parameters are accurately known, significant intelligibility gains, up to 40%, result in the processed speech signal. These parameters can be also estimated from noisy data, resulting into smaller but significant intelligibility gains.

A new audio transform coding technique is proposed that reduces the bitrate requirements of the perceptual transform audio coders by utilizing the stationarity characteristics of the audio signals. The method detects the frames that have significant audible content and codes them in a way similar to conventional perceptual transform coders. However, when successive data frames are found to be similar to those sections, then their audible differences only are coded. An error analysis for the proposed method is presented and results from tests on different types of audio material are listed, indicating that an average of 30% in compression gain (over the conventional perceptual audio coders bitrate) can be achieved, with a small deterioration in the audio quality of the coded signal. The proposed method has the advantage of easy adaptation within the perceptual transform coders architecture and adds only a small computational overhead to these systems.

A vector quantization (VQ) technique for room acoustics applications is described. Such an approach allows classification of all possible room transfer functions (RTFs) corresponding to different source/receiver positions into discrete and representative frequency response patterns. Results are presented for simulated tests in three rectangular enclosures, for which the optimum number of such patterns (centroids) is estimated. The performance of the proposed method is also assessed by deconvolution which indicates the degree of similarity between all classified RTFs and their corresponding centroids. The VQ technique achieved the required classification in all tested cases. Typically, 256 centroids were required for a medium-sized room, resulting in approximate reduction of 20:1 over all possible RTF measurements inside such an enclosure. The VQ performance was better for central source positions than for the corner positions. In all cases RTF smoothing could be observed after deconvolution by the inverse of the centroid's spectrum, indicating that the VQ technique is suitable for dereverberation applications.>

Acoustic environments can be treated as linear systems whose transmission properties are given by their impulse response functions. This basic model can be extended, under certain conditions, to describe the relationship between the envelopes of the input and output waveforms. Such a model is proposed in the context of a multiband processing scheme, aiming at the enhancement of single microphone recorded reverberant speech signals. The specific requirements of this model permit a simplified approach to the estimation of the envelope functions. The model allows accurate evaluation of the envelope of the reverberant speech, even when little prior information about the room characteristics is available. Speech enhancement can be then achieved after envelope deconvolution in each band, which recovers the envelope of the anechoic signal from the measured speech envelope, and final reconstruction of the speech waveform using the original phase function.

The inversion of minimum phase signals is well understood. However, many signals are non-minimum phase and the deconvolution of such sequences is less well documented. The aim of this paper is to compare two of the main techniques for inversion of finite length sequences, namely, homomorphic and least squares. Homomorphic methods require separation of the sequence into minimum and maximum phase components prior to inversion. On the other hand, least squares methods are applicable directly to the time sequence. However, the usual formulation applied to mixed phase signals produces an output having an all-pass form, but the use of delay yields an operator which can approximately invert mixed phase inputs. A brief summary of the two approaches is given, followed by a detailed comparison of the methods as applied to several case studies.