Microphone Arrays

By Philippos Mordohai, Gérard G. Medioni, Pascal Fua, Arun A. Ross, Jung Soh, Farzin Deravi, Alessandro Triglia, A. Bazin, Fabio Roli, J. Bigun and 2 more. Encyclopedia of Biometrics, 2009

349 citations recorded by the source corpus.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/3ea25405fafceca923208a9d2bbe7b53599a86f4

How researchers see this paper

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

In Signal Processing

1. Mirco Ravanelli
2. Yoshua Bengio
3. Maurizio Omologo
4. Philemon Brakel
5. Piergiorgio Svaizer
6. Antoine Liutkus
7. Emmanuel Vincent
8. Aditya Arie Nugraha

Citations in the corpus

• Multichannel Audio Source Separation With Deep Neural Networks (2016, background): “The combination of time alignment and channel averaging is known as DS beamforming in the microphone array literature [45], [46].”
• Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, background): “Considerable progresses were fostered by substantial advances in spatial filtering [6, 7], microphone selection [8], source separation [9], speech dereverberation [10] as well as speaker localization [11], acoustic event detection [12] and acoustic modeling.”
• Direction of Arrival Based Spatial Covariance Model for Blind Sound Source Separation (2014, neutral): “The examples of such applications include spatial audio coding (SAC) [1], [2], 3D sound analysis and synthesis [3], and signal enhancement for various purposes, such as automatic speech recognition (ASR) [4], [5].”
• On the selection of the impulse responses for distant-speech recognition based on contaminated speech training (2014, background): “The literature in this field reports several possible approaches, which include the adoption of microphone arrays and related beamforming techniques, in order to perform a spatial filtering that emphasizes the speech input, while attenuating environmental noise and reverberation effects [4, 5].”
• Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, background): “Despite the recent substantial progress in various related fields, including spatial filtering [1, 2], microphone selection [3], source separation [4], speech dereverberation [5], speaker localization [6], acoustic event detection [7] as well as acoustic modeling [8, 9, 10, 11, 12], DSR still…”
• The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, background): “This section reports the results obtained with a standard delay-and-sum beamforming [4] applied to both the ceiling and the harmonic arrays of the living-room.”
• Microphone array processing for distant speech recognition: Towards real-world deployment (2012, background): “This contribution complements Kumatani et al [9] with the new DSR experiments on the Kinect data.”
• A network of deep neural networks for Distant Speech Recognition (2017, background): “Index Terms— speech recognition, speech enhancement, joint training, deep neural networks”
• Batch-normalized joint training for DNN-based distant speech recognition (2016, background): “…even the most recent DSR systems [9] must rely on a combination of several interconnected technologies, including for instance speech enhancement [10], speech separation [11], acoustic event detection and classification [12, 13], speaker identification [14], speaker localization [15, 16], just to…”