Microphone Arrays Signal Processing Techniques and Applications

By M. Brandstein, Darren B. Ward. 2001

1,478 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a7c2244ec4bbb0210d2748cfc57086de285830ae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bo Li
2. Tara N. Sainath
3. Jinyu Li
4. Dong Yu
5. Yoshua Bengio
6. Yu Zhang
7. Arun Narayanan
8. Chuang Gan

Citations in the corpus

• Multichannel Eigenspace Beamforming in a Reverberant Noisy Environment With Multiple Interfering Speech Signals (2009, background): “As beamformer algorithms are very often followed by a postfilter [35, 36], and since postfilters are sensitive to nonstationary noise signals, it is crucial to maintain the residual noise level as stable as possible.”
• EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, background): “constructed functional networks with magnitude squared coherence estimation [23], partial direct coherence (PDC) [24], and phase slope index [25], and extracted features from these networks for four emotions (happy, angry, sad and relax) in preand post-meditation states.”
• Supervised Speech Separation Based on Deep Learning: An Overview (2017, unknown): “ separation. Beamforming, or spatial filtering, boosts the signal that arrives from a specific direction through proper array configuration, hence attenuating interference from other directions [164] [14] [9] [88]. The simplest beamformer is a delay-and-sum technique that adds multiple microphone si”
• Particle filtering algorithms for tracking an acoustic source in a reverberant environment (2003, background): “In this paper we are particularly interested in speech, where applications include automatic camera steering for video-conferencing, discriminating between individual talkers in multisource environments, and providing steering information for microphone arrays [ 1 ].”
• The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings (2013, unknown): “As a result, there is continued interest in developing methods to disentangle some sound of interest from that background noise [1, 2, 3].”
• A Survey of Sound Source Localization with Deep Learning Methods (2021, unknown): “Although they showed notable advances in the domain over the years, they are known to perform poorly in difficult yet common scenarios where noise, reverberation and several simultaneously emitting sound sources may be present [8], [9].”
• Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition (2016, background): “While automatic speech recognition (ASR) performance has improved dramatically in recent years, particularly with the advent of deep learning [5], performance in realistic noisy and far-field scenarios is still far-behind clean speech conditions [6, 7, 8] To improve robustness, microphone arrays are”
• Self-Supervised Moving Vehicle Tracking With Stereo Sound (2019, background): “Passive audio localization technology typically involves using microphone arrays and beam-forming techniques [11].”
• Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition (2017, methodology): “Commonly used filter design criteria are based on Minimum Variance Distortionless Response (MVDR) [3], [6] or multichannel Wiener filtering (MWF) [1].”
• A two-stage algorithm for one-microphone reverberant speech enhancement (2006, background): “A number of reverberant speech enhancement algorithms have been designed to perform in unknown acoustic environments but utilize more than one microphone.”