Blind MultiChannel Identification and Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function

This paper addresses the problems of blind channel identification and multichannel equalization for speech dereverberation and noise reduction. The time-domain cross-relation method is not suitable for blind room impulse response identification, due to the near-common zeros of the long impulse responses. We extend the cross-relation method to the short-time Fourier transform (STFT) domain, in which the time-domain impulse responses are approximately represented by the convolutive transfer functions (CTFs) with much less coefficients. The CTFs suffer from the common zeros caused by the oversampled STFT. We propose to identify CTFs based on the STFT with the oversampled signals and the critical sampled CTFs, which is a good compromise between the frequency aliasing of the signals and the common zeros problem of CTFs. In addition, a normalization of the CTFs is proposed to remove the gain ambiguity across sub-bands. In the STFT domain, the identified CTFs is used for multichannel equalization, in which the sparsity of speech signals is exploited. We propose to perform inverse filtering by minimizing the $\ell_1$-norm of the source signal with the relaxed $\ell_2$-norm fitting error between the micophone signals and the convolution of the estimated source signal and the CTFs used as a constraint. This method is advantageous in that the noise can be reduced by relaxing the $\ell_2$-norm to a tolerance corresponding to the noise power, and the tolerance can be automatically set. The experiments confirm the efficiency of the proposed method even under conditions with high reverberation levels and intense noise.Comment: 13 pages, 5 figures, 5 table

Virtual Audio - Three-Dimensional Audio in Virtual Environments

Three-dimensional interactive audio has a variety ofpotential uses in human-machine interfaces. After lagging seriously behind the visual components, the importance of sound is now becoming increas-ingly accepted. This paper mainly discusses background and techniques to implement three-dimensional audio in computer interfaces. A case study of a system for three-dimensional audio, implemented by the author, is described in great detail. The audio system was moreover integrated with a virtual reality system and conclusions on user tests and use of the audio system is presented along with proposals for future work at the end of the paper. The thesis begins with a definition of three-dimensional audio and a survey on the human auditory system to give the reader the needed knowledge of what three-dimensional audio is and how human auditory perception works

Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

Short-range ultrasonic communications in air using quadrature modulation

A study has been undertaken of ultrasonic communications methods in air, using a quadrature modulation method. Simulations were first performed to establish the likely performance of quadrature phase shift keying over the limited bandwidth available in an ultrasonic system. Quadrature phase shift keying modulation was then implemented within an experimental communication system, using capacitive ultrasonic sources and receivers. The results show that such a system is feasible in principle for communications over distances of several meters, using frequencies in the 200 to 400 kHz range

Robust equalization of multichannel acoustic systems

In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved by equalizing the channels from the source to microphones. Equalization systems can be computed using estimates of multichannel acoustic impulse responses. However, the estimates obtained from system identification always include errors; the fact that an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this thesis is to propose and investigate robust equalization methods for multichannel acoustic systems in the presence of system identification errors. Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems obtained from these methods are very sensitive to system identification errors. A study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least- squares method is proposed. Channel shortening in connection with the multiple- input/output inverse theorem and the relaxed multichannel least-squares method is discussed. Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization method exploring the use of error models is presented, which incorporates system identification error models in the weighted multichannel least-squares formulation

Speech Transmission Index from running speech : a neural network approach

Speech Transmission Index (STI) is an important objective parameter concerning speech intelligibility for sound transmission channels. It is normally measured with specific test signals to ensure high accuracy and good repeatability. Measurement with running speech was previously proposed, but accuracy is compromised and hence applications limited. A new approach that uses artificial neural networks to accurately extract the STI from received running speech is developed in this paper. Neural networks are trained on a large set of transmitted speech examples with prior knowledge of the transmission channels' STIs. The networks perform complicated nonlinear function mappings and spectral feature memorization to enable accurate objective parameter extraction from transmitted speech. Validations via simulations demonstrate the feasibility of this new method on a one-net-one-speech extract basis. In this case, accuracy is comparable with normal measurement methods. This provides an alternative to standard measurement techniques, and it is intended that the neural network method can facilitate occupied room acoustic measurements