Zobrazeno 1 - 10
of 3 528
pro vyhledávání: '"STERN, A. M."'
The Full Bayesian Significance Test (FBST) possesses many desirable aspects, such as not requiring a non-zero prior probability for hypotheses while also producing a measure of evidence for $H_0$. Still, few attempts have been made to bring the FBST
Externí odkaz:
http://arxiv.org/abs/2406.15608
Autor:
Yin, Hanzhi, Cheng, Gang, Steinmetz, Christian J., Yuan, Ruibin, Stern, Richard M., Dannenberg, Roger B.
We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applicati
Externí odkaz:
http://arxiv.org/abs/2403.16331
Data collection and annotation is a laborious, time-consuming prerequisite for supervised machine learning tasks. Online Active Learning (OAL) is a paradigm that addresses this issue by simultaneously minimizing the amount of annotation required to t
Externí odkaz:
http://arxiv.org/abs/2309.14460
Autor:
Ma, Yinghao, Stern, Richard M.
While end-to-end systems are becoming popular in auditory signal processing including automatic music tagging, models using raw audio as input needs a large amount of data and computational resources without domain knowledge. Inspired by the fact tha
Externí odkaz:
http://arxiv.org/abs/2211.15254
This paper studies whether Bayesian simultaneous three-way hypothesis tests can be logically coherent. Two types of results are obtained. First, under the standard error-wise constant loss, only for a limited set of models can a Bayes simultaneous te
Externí odkaz:
http://arxiv.org/abs/2204.01495
Autor:
Jarrar, Randa, Stern, John M., Becker, Danielle A., Davis, Charles, Rabinowicz, Adrian L., Carrazana, Enrique
Publikováno v:
In Epilepsy & Behavior October 2024 159
We describe a modulation-domain loss function for deep-learning-based speech enhancement systems. Learnable spectro-temporal receptive fields (STRFs) were adapted to optimize for a speaker identification task. The learned STRFs were then used to calc
Externí odkaz:
http://arxiv.org/abs/2102.07330
Autor:
Kambanis, P. Evelyna, Tabri, Nassim, McPherson, Iman, Gydus, Julia E., Kuhnle, Megan, Stern, Casey M., Asanza, Elisa, Becker, Kendra R., Breithaupt, Lauren, Freizinger, Melissa, Shrier, Lydia A., Bern, Elana M., Eddy, Kamryn T., Misra, Madhusmita, Micali, Nadia, Lawson, Elizabeth A., Thomas, Jennifer J.
Publikováno v:
In Journal of the American Academy of Child & Adolescent Psychiatry April 2024
In this paper we demonstrate the effectiveness of non-causal context for mitigating the effects of reverberation in deep-learning-based automatic speech recognition (ASR) systems. First, the value of non-causal context using a non-causal FIR filter i
Externí odkaz:
http://arxiv.org/abs/2009.02832
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.