Zobrazeno 1 - 10
of 296
pro vyhledávání: '"Sharon, Gannot"'
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-15 (2024)
Abstract This paper addresses the challenge of online blind speaker separation in a multi-microphone setting. The linearly constrained minimum variance (LCMV) beamformer is selected as the backbone of the separation algorithm due to its distortionles
Externí odkaz:
https://doaj.org/article/28ecf5174c4446aeba3e9f34d74bd140
Autor:
Yarden Menashri Sinai, Yaopeng X. J. Ma, Michal Abba Daleski, Sharon Gannot, Ronny P. Bartsch, Ilanit Gordon
Publikováno v:
Frontiers in Human Neuroscience, Vol 18 (2024)
IntroductionTo date, studies focusing on the connection between psychological functioning and autonomic nervous system (ANS) activity usually adopted the one-dimensional model of autonomic balance, according to which activation of one branch of the A
Externí odkaz:
https://doaj.org/article/3cfb9ba697d7434d8ef47fa80d5d4564
Autor:
Ofer Schwartz, Sharon Gannot
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-15 (2021)
Abstract The problem of blind and online speaker localization and separation using multiple microphones is addressed based on the recursive expectation-maximization (REM) procedure. A two-stage REM-based algorithm is proposed: (1) multi-speaker direc
Externí odkaz:
https://doaj.org/article/4f4a7b3866354213a203652554fc6ef2
Autor:
Diego Di Carlo, Pinchas Tandeitnik, Cedrić Foy, Nancy Bertin, Antoine Deleforge, Sharon Gannot
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-15 (2021)
Abstract This paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall con
Externí odkaz:
https://doaj.org/article/81740a755b88415b93a6ac6c56dc9de6
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-17 (2021)
Abstract Many modern smart devices are equipped with a microphone array and a loudspeaker (or are able to connect to one). Acoustic echo cancellation algorithms, specifically their multi-microphone variants, are essential components in such devices.
Externí odkaz:
https://doaj.org/article/995d273cdbd9485a9602db607a8978c8
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-10 (2021)
Abstract In this study, we present a deep neural network-based online multi-speaker localization algorithm based on a multi-microphone array. Following the W-disjoint orthogonality principle in the spectral domain, time-frequency (TF) bin is dominate
Externí odkaz:
https://doaj.org/article/65f5263b056245d0901e2e1a6c0a5ad7
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-13 (2021)
Abstract In this paper, a study addressing the task of tracking multiple concurrent speakers in reverberant conditions is presented. Since both past and future observations can contribute to the current location estimate, we propose a forward-backwar
Externí odkaz:
https://doaj.org/article/ef956edbd62b418da4efffc38514d1fd
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2021, Iss 1, Pp 1-22 (2021)
Abstract This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors. We assume a n
Externí odkaz:
https://doaj.org/article/d9d188dab150478081bb0ae2d66a2a8e
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-16 (2021)
Abstract Two novel methods for speaker separation of multi-microphone recordings that can also detect speakers with infrequent activity are presented. The proposed methods are based on a statistical model of the probability of activity of the speaker
Externí odkaz:
https://doaj.org/article/47c121c17e604358be70bba359acd35d
Publikováno v:
IEEE Access, Vol 9, Pp 84956-84970 (2021)
Localization in reverberant environments remains an open challenge. Recently, supervised learning approaches have demonstrated very promising results in addressing reverberation. However, even with large data volumes, the number of labels available f
Externí odkaz:
https://doaj.org/article/86cf7072d8494dedb4f80e453819a0b0