Multichannel Signal Enhancement Algorithms for Assisted Listening Devices: Exploiting spatial diversity using multiple microphones

Source separation employing beamforming and SRP-PHAT localization in three-speaker room environments

“…Over the last 10-15 years research in machine interfaces for voice pick-up in reverberant and noisy environments has been very actively conducted using multi-channel systems [1][2][3][4]. Multi-channel techniques have been useful in many applications such as hearing aids, handsfree communication, robotics, audio and video conference systems, and speech recognition [1,2,5,6].…”

“…Multi-channel techniques have been useful in many applications such as hearing aids, handsfree communication, robotics, audio and video conference systems, and speech recognition [1,2,5,6]. One of the most popular techniques applied to multi-microphone systems is the optimal beamforming technique [1].…”

“…One of the most popular techniques applied to multi-microphone systems is the optimal beamforming technique [1]. Optimal beamformers are formulated to exploit spatial information of desired and undesired signals in such a way that the desired one is extracted and undesired signals are suppressed [1,2]. Many methods have been proposed for determining the location of the desired source such as predefined well-determined array geometry combined with source localization [7,8], a calibration method using training samples of pre-recording desired and undesired sources [9,10].…”

“…These estimates are then used to form the optimal beamformer weights; for this method to be efficient a priori knowledge about the statistical characteristics of the noise is necessary. When the background noise is stationary over the measurement period either a voice activity detector (VAD) [2] estimate or a relative transfer function (RTF) estimate can be found [13]. Either of these estimates can be used to form optimal beamformers [2].…”

“…When the background noise is stationary over the measurement period either a voice activity detector (VAD) [2] estimate or a relative transfer function (RTF) estimate can be found [13]. Either of these estimates can be used to form optimal beamformers [2]. This leads us to a more general case where the spatial knowledge is not known a priori and the observed mixture signals are the only available information to be used for speech separation and noise suppression.…”