scholarly journals Multichannel speaker interference reduction using frequency domain adaptive filtering

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Patrick Meyer ◽  
Samy Elshamy ◽  
Tim Fingscheidt
Keyword(s):  
Wiener Filter ◽  
Echo Cancellation ◽  
Filter Method ◽  
Noise Component ◽  
Interference Reduction ◽  
Live Music ◽  
Audio Recordings ◽  
Automatic Transcription ◽  
Component Estimation ◽  
Filter Approach

Abstract Microphone leakage or crosstalk is a common problem in multichannel close-talk audio recordings (e.g., meetings or live music performances), which occurs when a target signal does not only couple into its dedicated microphone, but also in all other microphone channels. For further signal processing such as automatic transcription of a meeting, a multichannel speaker interference reduction is required in order to eliminate the interfering speech signals in the microphone channels. The contribution of this paper is twofold: First, we consider multichannel close-talk recordings of a three-person meeting scenario with various different crosstalk levels. In order to eliminate the crosstalk in the target microphone channel, we extend a multichannel Wiener filter approach, which considers all individual microphone channels. Therefore, we integrate an adaptive filter method, which was originally proposed for acoustic echo cancellation (AEC), in order to obtain a well-performing interferer (noise) component estimation. This results in an improved speech-to-interferer ratio by up to 2.7 dB at constant or even better speech component quality. Second, since an AEC method requires typically clean reference channels, we investigate and report findings why the AEC algorithm is able to successfully estimate the interfering signals and the room impulse responses between the microphones of the interferer and the target speakers even though the reference signals are themselves disturbed by crosstalk in the considered meeting scenario.

scholarly journals Improving Speech Quality for Hearing Aid Applications Based on Wiener Filter and Composite of Deep Denoising Autoencoders

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 138-156
Author(s):  
Raghad Yaseen Lazim ◽  
Zhu Yun ◽  
Xiaojun Wu
Keyword(s):  
Hearing Loss ◽  
Signal To Noise Ratio ◽  
Hearing Aid ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Wiener Filter ◽  
Speech Quality ◽  
Denoising Autoencoder ◽  
Noise Component ◽  
Perceptual Evaluation

In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, a single DDAE cannot extract contextual information sufficiently due to the poor generalization in an unknown signal-to-noise ratio (SNR), the local minima, and the fact that the enhanced output shows some residual noise and some level of discontinuity. In this paper, we propose a hybrid approach for hearing aid applications based on two stages: (1) the Wiener filter, which attenuates the noise component and generates a clean speech signal; (2) a composite of three DDAEs with different window lengths, each of which is specialized for a specific enhancement task. Two typical high-frequency hearing loss audiograms were used to test the performance of the approach: Audiogram 1 = (0, 0, 0, 60, 80, 90) and Audiogram 2 = (0, 15, 30, 60, 80, 85). The hearing-aid speech perception index, the hearing-aid speech quality index, and the perceptual evaluation of speech quality were used to evaluate the performance. The experimental results show that the proposed method achieved significantly better results compared with the Wiener filter or a single deep denoising autoencoder alone.

A Wiener filter approach to shape from shading

2017 ◽  
Author(s):  
Oscar E. Castillo ◽  
Jorge Luis Flores Nuñez ◽  
Jose A. Muñoz ◽  
Ricardo Legarda
Keyword(s):  
Wiener Filter ◽  
Shape From Shading ◽  
Filter Approach

Distinction of Glossy Colored Objects Using Gray Level

1992 ◽  
Vol 4 (6) ◽  
pp. 511-519
Author(s):  
Kazuo Yamaba ◽  
◽  
Yoichi Miyake ◽  
Keyword(s):  
Color Image ◽  
Wiener Filter ◽  
Psychophysical Method ◽  
Filter Method ◽  
Zoom Lens ◽  
Measuring Apparatus ◽  
Blurred Image ◽  
Mirror Box ◽  
Tristimulus Values ◽  
Lamp Illumination

A new measuring apparatus for color images has been developed in order to distinguish glossy colored objects under fluorescent lamp illumination. The color image apparatus is mainly composed of a zoom lens, a mirror box, MOS cameras, a microcomputer and a color image processor. The zoom lens can automatically blur an image by the microcomputer. It is a very effective method for obtaining a blurred image in detecting glossy colored objects. Gloss can be omitted by blurring an original image of objects. In the blurred image, it is known that tristimulus values are not affected by image restoration if the modified Wiener filter method is employed. The blurred image is restored by the filter and is processed by a new analogical method utilizing a fuzzy set theory based on a visual psychophysical method. As a result of this experiment, it can be concluded that the system demonstrates the possibility of highly accurate distinction of glossy colored objects.

A KALMAN FILTER APPROACH TO THE DECONVOLUTION OF SEISMIC SIGNALS

Geophysics ◽  
1974 ◽  
Vol 39 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Norman D. Crump
Keyword(s):  
Kalman Filter ◽  
Wiener Filter ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Discrete State ◽  
Seismic Wavelet ◽  
State Variable ◽  
Deconvolution Technique ◽  
Reflectivity Function ◽  
Filter Approach

It is common practice to model a reflection seismogram as a convolution of the reflectivity function of the earth and an energy waveform referred to as the seismic wavelet. The objective of the deconvolution technique described here is to extract the reflectivity function from the reflection seismogram. The most common approach to deconvolution has been the design of inverse filters based on Wiener filter theory. Some of the disadvantages of the inverse filter approach may be overcome by using a state variable representation of the earth’s reflectivity function and the seismic signal generating process. The problem is formulated in discrete state variable form to facilitate digital computer processing of digitized seismic signals. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The principal advantages of this technique are its capability for handling continually time‐varying models, its adaptability to a large class of models, its suitability for either single or multi‐channel processing, and its potentially high‐resolution capabilities. Examples based on both synthetic and field seismic data illustrate the feasibility of the method.

scholarly journals Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Govind Kannan ◽  
Issa M. S. Panahi ◽  
Richard W. Briggs
Keyword(s):  
Noise Control ◽  
Acoustic Noise ◽  
Random Noise ◽  
Active Noise Control ◽  
Superior Performance ◽  
Filter Method ◽  
Periodic Signal ◽  
Noise Component ◽  
Periodic Noise ◽  
Active Noise

A large class of acoustic noise sources has an underlying periodic process that generates a periodic noise component, and thus their acoustic noise can in general be modeled as the sum of a periodic signal and a randomly fluctuating signal (usually a broadband background noise). Active control of periodic noise (i.e., for a mixture of sinusoids) is more effective than that of random noise. For mixtures of sinusoids in a background broadband random noise, conventional FXLMS-based single filter method does not reach the maximum achievable Noise Attenuation Level (NALmax⁡). In this paper, an alternative approach is taken and the idea of a parallel active noise control (ANC) architecture for cancelling mixtures of periodic and random signals is presented. The proposed ANC system separates the noise into periodic and random components and generates corresponding antinoises via separate noise cancelling filters, and tends to reach NALmax⁡ consistently. The derivation of NALmax⁡ is presented. Both the separation and noise cancellation are based on adaptive filtering. Experimental results verify the analytical development by showing superior performance of the proposed method, over the single-filter approach, for several cases of sinusoids in white noise.

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