Adaptive beamformer

About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.

Low-complexity variable loading for robust adaptive beamforming

Abstract: Although the majority of the existing robust adaptive beamformers focus on how to choose an identical loading factor for all eigenvalues, relatively few investigations on variable loading (VL) have been conducted. A low-complexity VL beamformer is proposed in which the beamformer weight vector is deliberately prevented from converging to the noise components. Then inspired by the traditional identical loading method, the loading factor can be set in an ad hoc manner. Numerical results demonstrate the superior performance of the proposed beamformer relative to other existing approaches such as ‘identical diagonal loading’ and ‘robust Capon’ beamformers.

Topics in acoustic echo and noise control : selected methods for the cancellation of acoustical echoes, the reduction of background noise, and speech processing ; with 32 tables

Abstract: Acoustic Echo and Noise Control - Where did we come from and where are we going?.- Multi-Microphone Processing.- Joint Optimization of Acoustic Echo Cancellation and Adaptive Beamforming.- Blind Source Separation of Convolutive Mixtures of Audio Signals in Frequency Domain.- Localization and Tracking of Acoustical Sources.- Echo Cancellation.- Adaptive Algorithms for the Identification of Sparse Impulse Responses.- Selective-Tap Adaptive Algorithms for Echo Cancellation.- Nonlinear Acoustic Echo Cancellation.- Intelligent Control Strategies for Hands-Free Telephones.- Noise Reduction.- Noise Reduction.- Noise Reduction with Kalman-Filters for Hands-Free Car Phones Based on Parametric Spectral Speech and Noise Estimates.- Selected Applications.- Evaluation of Algorithms for Speech Enhancement.- An Auditory Scene Analysis Approach to Monaural Speech Segregation.- Wave Field Synthesis Techniques for Spatial Sound Reproduction.- Signal Processing for In-Car Communication Systems.- Applications of Adaptive Signal Processing Methods in High-End Hearing Aids.

Probabilistic spatial dictionary based online adaptive beamforming for meeting recognition in noisy and reverberant environments

Abstract: Here we propose online adaptive beamforming for automatic speech recognition (ASR) in meetings in noisy, reverberant environments. The proposed method is based on recently developed mask-based beamforming, in which accurate mask estimation and diarization are paramount. Real-world experiments have shown that mask-based beamforming enables accurate ASR in meetings in small noise and reverberation with a signal-to-noise ratio (SNR) of 15–25 dB and a reverberation time (RT) of 120–350 ms. In this paper, we deal with a more adverse condition: meetings in large noise and reverberation with an SNR of 3–15 dB and an RT of 500 ms. To this end, we exploit a probabilistic spatial dictionary, a dictionary that consists of a pre-trained probability distribution of source location features for each potential speaker location. This dictionary enables us to perform mask estimation and diarization for beamforming accurately, even in the above adverse condition. The proposed method reduced the word error rate (WER) on real meeting data by 54.8% relative to our previous beamforming method.

Adaptive Beamforming with Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean PSO

Abstract: A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train e-ciently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.

Ultrasound adaptor methods and systems for transducer and system separation

Abstract: Methods and systems are provided for adapting signals from an ultrasound transducer for an ultrasound system. Where the signal processing in a transducer assembly outputs data incompatible with the ultrasound system, circuitry provided within the transducer assembly converts the data to be compatible with the ultrasound systems. For example, sub-array mixing is provided to partially beamform signals from a plurality of transducer elements. The resulting output signals from a plurality sub-arrays are provided through a cable to a connector housing of the transducer probe assembly. Since the mixers alter the data, such as shifting the data to an intermediate frequency, the output data may be at a frequency different than the frequencies for operation of the receive beamformer. Additional mixers are then provided to convert the intermediate frequency signals to radio frequency signals that may be processed by the ultrasound systems received beamformer. As another example, signals from a plurality of transducer elements are multiplexed together. Where the receive beamformer is not operable to de-multiplex such signals, circuitry within the transducer probe assembly converts the signals by de-multiplexing the data for beamforming. Ultrasound systems have a limited number of received beamformer channels. By providing signal processing, conversion, and/or partial beamforming within the transducer probe assembly, the number of elements used may be different than the number of received beamformer channels provided by the system.