Independent Component Analysis.

Binary time-frequency masks are powerful tools for the separation of sources from a single mixture. Perfect demixing via binary time-frequency masks is possible provided the time-frequency representations of the sources do not overlap: a condition we call W-disjoint orthogonality. We introduce here the concept of approximate W-disjoint orthogonality and present experimental results demonstrating the level of approximate W-disjoint orthogonality of speech in mixtures of various orders. The results demonstrate that there exist ideal binary time-frequency masks that can separate several speech signals from one mixture. While determining these masks blindly from just one mixture is an open problem, we show that we can approximate the ideal masks in the case where two anechoic mixtures are provided. Motivated by the maximum likelihood mixing parameter estimators, we define a power weighted two-dimensional (2-D) histogram constructed from the ratio of the time-frequency representations of the mixtures that is shown to have one peak for each source with peak location corresponding to the relative attenuation and delay mixing parameters. The histogram is used to create time-frequency masks that partition one of the mixtures into the original sources. Experimental results on speech mixtures verify the technique. Example demixing results can be found online at http://alum.mit.edu/www/rickard/bss.html.

Blind separation of speech mixtures via time-frequency masking

Active noise control exploits the long wavelengths associated with low frequency sound. It works on the principle of destructive interference between the sound fields generated by the original primary sound source and that due to other secondary sources, acoustic outputs of which can be controlled. The acoustic objectives of different active noise control systems and the electrical control methodologies that are used to achieve these objectives are examined. The importance of having a clear understanding of the principles behind both the acoustics and the electrical control in order to appreciate the advantages and limitations of active noise control is emphasized. A brief discussion of the physical basis of active sound control that concentrates on three-dimensional sound fields is presented. >

Active noise control

On typical echo paths, the proportionate normalized least-mean-squares (PNLMS) adaptation algorithm converges significantly faster than the normalized least-mean-squares (NLMS) algorithm generally used in echo cancelers to date. In PNLMS adaptation, the adaptation gain at each tap position varies from position to position and is roughly proportional at each tap position to the absolute value of the current tap weight estimate. The total adaptation gain being distributed over the taps is carefully monitored and controlled so as to hold the adaptation quality (misadjustment noise) constant. PNLMS adaptation only entails a modest increase in computational complexity.

Proportionate normalized least-mean-squares adaptation in echo cancelers

There has been a great deal of material in the area of discrete-time transforms that has been published in recent years. This book does an excellent job of presenting important aspects of such material in a clear manner. The book has 11 chapters and a very useful appendix. Seven of these chapters are essentially devoted to the Fourier series/transform, discrete Fourier transform, fast Fourier transform (FFT), and applications of the FFT in the area of spectral estimation. Chapters 8 through 10 deal with many other discrete-time transforms and algorithms to compute them. Of these transforms, the KarhunenLoeve, the discrete cosine, and the Walsh-Hadamard transform are perhaps the most well-known. A lucid discussion of number theoretic transforms i5 presented in Chapter 11. This reviewer feels that the authors have done a fine job of compiling the pertinent material and presenting it in a concise and clear manner. There are a number of problems at the end of each chapter, an appreciable number of which are challenging. The authors have included a comprehensive set of references at the end of the book. In brief, this book is a valuable contribution to the general areas of signal processing and communications. It can be used for a graduate level course in perhaps two ways. One would be to cover the first seven chapters in great detail. The other would be to cover the whole book by focussing on different topics in a selective manner. This book  by  Elliott  and Rao  is extremely  useful to researchers/engineers who are working  in the areas of signal processing and communications. It i s also an excellent reference book, and hence a valuable addition to one’s library

http://ieeexplore.ieee.org/iel5/5/31347/01457444.pdf

Multirate digital signal processing

Even if received signals are highly cross-correlated, echoes can be effectively cancelled and no psychoacoustical problems arise. A received signal x i (k) (where i=1, 2, . . . , N) and an additive signal a i (k) are added together, and the added output is used to drive a speaker i and input into an echo cancellation filter 405 i . The received signal x i (k) and the additive signal a i (k) are input into adaptive filters 401 i and 402 i , respectively. The difference between the sum of the outputs from all the filters 401 i and all the filters 402 i and an echo y m (k) is detected as an error e m (k). The coefficients of all the filters 401 i and 402 i are updated to reduce the error e m (k). When the error e m (k) is made sufficiently small, the coefficients of the filters 402 i are transferred to the filters 405 i . The sum of the outputs from all the filters 405 i is detected as an echo replica, and the difference between the echo replica and the echo y m (k) is output.

Method and apparatus for multi-channel acoustic echo cancellation and recording medium with the method recorded thereon

A received signal vector x(n), a coefficient error covariance matrix P(n) from a memory part 12 and a forgetting factor ν from a memory part 13 are provided to a gain calculating part 14 to obtain a gain vector k(n). The thus obtained gain vector k(n) and an error e(n) between an echo and an echo replica are multiplied in a multiplying part 16. The multiplied output and a filter coefficient h(n) from a memory part 18 are added together to update the latter. The thus updated filter coefficient is used as the filter coefficient of an estimated echo path (an FIR filter, for example). The coefficient error covariance matrix P(n), the gain vector k(n), the received signal vector x(n) and the forgetting factor ν are provided to an updating part 19 to update the coefficient error covariance matrix P(n), and an adjustment matrix A representing an expectation of an impulse response variation of an echo path is added to the updated coefficient error covariance matrix P(n) and the added value is used as a new coefficient error covariance matrix P(n).

Echo cancelling method and echo canceller using the same

The sound data for open evaluation is necessary for studies such as sound source localization, sound retrieval, sound recognition and hands-free speech recognition in real acoustic environments. This paper reports on our project for acoustic data collection. There are many kinds of sound scenes in real environments. The sound scene is specified by sound sources and room acoustics. The number of combinations of the sound sources, source positions and rooms is huge in real acoustic environments. We assumed that the sound in the environments can be simulated by convolution of the isolated sound sources and impulse responses. As an isolated sound source, hundred kinds of environment sounds and speech sounds are collected. The impulse responses are collected in various acoustic environments. Additionally we collected sounds from a moving source. In this paper, progress of our sound scene database collection project and application to environment sound recognition and hands-free speech recognition are described.

Design and collection of acoustic sound data for hands-free speech recognition and sound scene understanding

/pdf/study-of-harmonic-distortion-on-impulse-response-measurement-lnbq8330kd.pdf

Study of harmonic distortion on impulse response measurement with logarithmic time stretched pulse

AMNOR is a new type of noise-reduction microphone system which uses a microphone arrayand digital signal processing. In this paper, the theory of AMNOR is described in the frequency domain, and its directivity characteristics are studied. Then, it is shown that, under some simple noise conditions, AMNOR forms the same directivity patterns as those formed by conventional directional microphones. In contrast to the directivity characteristics of conventional directional microphones which are assumed to be optimal for some restricted noise environments, AMNOR can realize the optimal directivity characteristics adaptively for a wide variety of existing noise environments. This is accomplished in AMNOR by the following three functions;(1) AMNOR forms M-1 (where M is the number of microphone elements) dead angles with respect to the noise arrival directions, (2) AMNOR optimizes the directivity lobe figure (3) AMNOR performs directivity control of (1) and (2) for each frequency band independently.

Yutaka Kaneda

Papers

Method and apparatus for multi-channel acoustic echo cancellation and recording medium with the method recorded thereon

Echo cancelling method and echo canceller using the same

Design and collection of acoustic sound data for hands-free speech recognition and sound scene understanding

Study of harmonic distortion on impulse response measurement with logarithmic time stretched pulse

Directivity characteristics of adaptive microphone-array for noise reduction (AMNOR)