Zobrazeno 1 - 10
of 1 609
pro vyhledávání: '"Stern, Richard A"'
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
Autor:
Partin, Camille A., McDonald, Brayden S., McConnell, Michael, Thrane, Kristine, Graham Pearson, D., Sarkar, Chiranjeeb, Luo, Yan, Stern, Richard A.
Publikováno v:
In Gondwana Research October 2024 134:222-244
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:
Jacob, Dorrit E., Stern, Richard A., Czas, Janina, Reutter, Magnus, Piazolo, Sandra, Stachel, Thomas
Publikováno v:
In Geochimica et Cosmochimica Acta October 2024
Voice Type Discrimination (VTD) refers to discrimination between regions in a recording where speech was produced by speakers that are physically within proximity of the recording device ("Live Speech") from speech and other types of audio that were
Externí odkaz:
http://arxiv.org/abs/2010.09151
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
Autor:
Carvalho, Luísa D.V., Stachel, Thomas, Pearson, D. Graham, Timmerman, Suzette, Stern, Richard A., Jalowitzki, Tiago, Scholz, Ricardo, Fuck, Reinhardt A.
Publikováno v:
In LITHOS 1 December 2023 460-461
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.