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

A recursive adaptive method of impulse response measurement using a new measurement signal, i.e., a CSN-SS (constant-SNR swept-sine) signal is proposed. In the method, the spectrum of a measurement signal is shaped, adapting to not only the background noise spectrum but also the recursively estimated transfer function of the system itself. The measurement results show a constant SNR independent of frequency over the target frequency band, in which the system response can be properly identified.

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A Recursive Adaptive Method of Impulse Response Measurement with Constant SNR over Target Frequency Band

equal to the number of poles. 3. Measured impulse responses Impulse responses were measured in an experimental room, changing the reverberation time. The room was rectangular with a volume of 87 m3. The rever-beration time was set to 0.3, 0.36, 0.55, 1.6, and 2.4 s in the 500 Hz octave band by adding or removing ab-sorbing materials to or from the walls. The upper frequency of the signal bands was changed to 100, 200, 300, 400, 500, 600, 800, and 1,000 Hz. The lower frequency of the bands was fixed to 60 Hz. The sampling frequency was 2.5 times the upper frequency of the frequency band. An example of the impulse responses is shown in Fig. 1. 4. ARMA modeling of the measured impulse re-sponses The measured impulse responses were modeled by a series-parallel type ARMA model as shown in Fig. 2. The ARMA model coefficients were estimated using the RLS algorithm5) so as to minimize the error e(k) between the real echo and the estimated echo. The order of the ARMA model (P+Q) was determined such that the power of the error e(k) normalized by the power of the real echo y(k) was -40 dB. Figure 3 shows experimental results. The vertical axis represents the orders of the ARMA and the MA models that give a ratio of the error power to the real echo power of -40 dB. The horizontal axis represents the tap length of the measured impulse responses where the reverberation level drops by -40 dB. Since the order of the MA model directly corresponds to the tap length of the impulse responses, the MA model requires as many taps as the impulse response has. On the other hand, the order of the ARMA model is found to be smaller than that of the MA model, especially for the low frequency band. For example, when modeling an impulse response whose band is from 60 200 Hz for a reverberation time of 1.8 s, the MA model re-quires 600 taps, but the ARMA model requires only 240 taps. In each frequency band, when the rever-beration time is long, the ARMA model can effectively reduce the orders. These results show that the ARMA model is more effective in the low frequency band and for long reverberation times for reducing the number

ARMA modeling of a room transfer function at low frequencies

PROBLEM TO BE SOLVED: To provide a noise reducing method and a device degraded little in tone quality in acoustic sense. SOLUTION: An input signal 15 is converted into a digital signal and then divided into a plurality of bands by a frequency band dividing part 22. The average power level of the input signal is then computed by an input signal power computing part 24. Abreast with this, noise power is estimated by a noise power computing part 31. In a loss value computing part 32, a noise ratio of each band occupied in a band signal is obtained from the average power level and noise power, and the loss value of each band is obtained on the basis of this ratio. The above-mentioned loss value is inserted in a loss vale inserting part 33, and after obtaining a noise reduced band output signal, this band output signal is converted into a time area by a time area converting part 28 and then converted into an analog signal to obtain a noise reduced output signal.

Noise reducing method and device

In the measurement of impulse response, ambient noise is included leading to a decrease in measurement quality To solve this problem the use of several measurement signals (or excitation signals) has been proposed However, the relationship between measurement signals and the noise reduction performance (NRP) has not been quantitatively examined thus far In this study the NRP characteristics of different measurement signals were theoretically examined to derive equations that can determine NRP from the spectra of the measurement signal and noise From the theoretical and experimental examinations the following results were obtained The NRP for white signals and noise-whitening signals is actually the same Only the minimum noise (MN) signal that minimizes the noise component showed a significant improvement in NRP A pink spectrum measurement signal showed good NRP in the presence of 1/k2 spectrum noise, where k is the discrete frequency number, but worse performance with other types of noise This supports the conclusion that using the MN signal, which has a power spectrum that is the square root of the power spectrum of the noise, is the best method of reducing the effect of noise on the measured impulse response

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Noise Reduction Performance of Various Signals for Impulse Response Measurement

PROBLEM TO BE SOLVED: To provide a sound pickup method and its device by which a sound is picked up by precisely directing the directivity of a microphone array in a direction of an object sound. SOLUTION: A signal picked up by a microphone array 21 is given to a microphone array master set 22, where the signal is processed, the directivity of the microphone array 21 is directed to an existing area of an object speaking person, to pick up an object voice signal with high S/N, and the picked-up voice signal is sent through a transmission line 23-2. A received signal from a communication destination via a reception line 23-1 is emitted from a reception loudspeaker 24 as a received voice signal. At this time, a reception detection section 30 detects a reception state, and a directivity control section 40 controls the directivity of the microphone array 21, based on the detected reception state.

Yutaka Kaneda

Papers

A Recursive Adaptive Method of Impulse Response Measurement with Constant SNR over Target Frequency Band

ARMA modeling of a room transfer function at low frequencies

Noise reducing method and device

Noise Reduction Performance of Various Signals for Impulse Response Measurement

Sound pickup method and its device