Employing sparsity in the joint estimation of sound source and acoustic channel parameters for natural sounds

2021 ◽  
Author(s):  
Travis Michael Doll
Keyword(s):  
Sound Source ◽  
Joint Estimation ◽  
Natural Sounds ◽  
Acoustic Channel ◽  
Channel Parameters

Target Detection in a Scenario of Natural Sounds: The Role of Spatial Sound Source Separation

2013 ◽  
Author(s):  
Susanne Mayr ◽  
Gunnar Regenbrecht ◽  
Kathrin Lange ◽  
Albertgeorg Lang ◽  
Axel Buchner
Keyword(s):  
Target Detection ◽  
Sound Source ◽  
Source Separation ◽  
Natural Sounds ◽  
Sound Source Separation ◽  
Spatial Sound

scholarly journals Joint estimation of channel parameters for very low signal-to-noise ratio environment in mobile radio propagations

Radio Science ◽  
2010 ◽  
Vol 45 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jingyu Hua ◽  
Limin Meng ◽  
Gang Li ◽  
Dongming Wang ◽  
Bin Sheng ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Mobile Radio ◽  
Joint Estimation ◽  
Signal To Noise ◽  
Channel Parameters ◽  
Noise Ratio

Estimation of underwater acoustic channel parameters for Erdek/Turkey region

2021 ◽  
Vol 181 ◽  
pp. 108135
Author(s):  
Mehmet Ali Çavuşlu ◽  
Mehmet Ali Altuncu ◽  
Hikmetcan Özcan ◽  
Fidan Kaya Gülağız ◽  
Suhap Şahin
Keyword(s):  
Underwater Acoustic Channel ◽  
Underwater Acoustic ◽  
Acoustic Channel ◽  
Channel Parameters

scholarly journals PARAFAC-Based Multiuser Channel Parameter Estimation for MmWave Massive MIMO Systems over Frequency Selective Fading Channels

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2983
Author(s):  
Rui Chang ◽  
Chaowei Yuan ◽  
Jianhe Du
Keyword(s):  
Fading Channels ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Joint Estimation ◽  
Selective Fading ◽  
Frequency Selective Fading ◽  
Training Sequences ◽  
Channel Parameters ◽  
Multiuser Channel ◽  
Frequency Selective Fading Channels

Channel estimation is crucial in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, especially with a few training sequences. To solve the problem of uplink channel estimation in mmWave massive MIMO systems, a PARAFAC-based algorithm is proposed for joint estimation of multiuser channels. The orthogonal frequency divisional multiplexing (OFDM) technique is exploited to combat the frequency selective fading channels. In this paper, the received signal at the base station (BS) is formulated as a third-order parallel factor (PARAFAC) tensor, and then a low-complexity algorithm is designed for fast estimation of the factor matrices related to channel parameters, thus leading to joint estimation of multiuser channel parameters via one-dimensional search. Moreover, the Cramér–Rao Bound (CRB) results for multiuser channel parameters are derived for evaluation. Theorical analysis and numerical results reveal that the algorithm performs well with a few training sequences. Compared with existing algorithms, the proposed algorithm has clear advantages both in estimation accuracy and computational complexity.

Role of pinnae and head movements in localizing pure tones 1The authors would like to thank Mr. Remi Humbert for implementing the experiment in Turbo Pascal and for his further assistance.

1999 ◽  
Vol 58 (3) ◽  
pp. 170-179 ◽  
Author(s):  
Barbara S. Muller ◽  
Pierre Bovet
Keyword(s):  
Sound Source ◽  
Head Movement ◽  
Movement Analysis ◽  
Head Movements ◽  
Sound Sources ◽  
Localization Accuracy ◽  
Sound Effects ◽  
Posterior Quadrant ◽  
Localization Performance ◽  
Pure Tones

Twelve blindfolded subjects localized two different pure tones, randomly played by eight sound sources in the horizontal plane. Either subjects could get information supplied by their pinnae (external ear) and their head movements or not. We found that pinnae, as well as head movements, had a marked influence on auditory localization performance with this type of sound. Effects of pinnae and head movements seemed to be additive; the absence of one or the other factor provoked the same loss of localization accuracy and even much the same error pattern. Head movement analysis showed that subjects turn their face towards the emitting sound source, except for sources exactly in the front or exactly in the rear, which are identified by turning the head to both sides. The head movement amplitude increased smoothly as the sound source moved from the anterior to the posterior quadrant.

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