Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Rao, K. Sreenivasa"'
Multilingual speaker verification introduces the challenge of verifying a speaker in multiple languages. Existing systems were built using i-vector/x-vector approaches along with Bi-LSTMs, which were trained to discriminate speakers, irrespective of
Externí odkaz:
http://arxiv.org/abs/2408.04362
We present a novel face swapping method using the progressively growing structure of a pre-trained StyleGAN. Previous methods use different encoder decoder structures, embedding integration networks to produce high-quality results, but their quality
Externí odkaz:
http://arxiv.org/abs/2310.12736
Melody extraction is a vital music information retrieval task among music researchers for its potential applications in education pedagogy and the music industry. Melody extraction is a notoriously challenging task due to the presence of background i
Externí odkaz:
http://arxiv.org/abs/2202.01078
Autor:
Mandalapu, Hareesh, N, Aravinda Reddy P, Ramachandra, Raghavendra, Rao, K Sreenivasa, Mitra, Pabitra, Prasanna, S R Mahadeva, Busch, Christoph
Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of the
Externí odkaz:
http://arxiv.org/abs/2109.04138
Autor:
Mandalapu, Hareesh, Reddy, P N Aravinda, Ramachandra, Raghavendra, Rao, K Sreenivasa, Mitra, Pabitra, Prasanna, S R Mahadeva, Busch, Christoph
Publikováno v:
in IEEE Access, vol. 9, pp. 37431-37455, 2021
Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent app
Externí odkaz:
http://arxiv.org/abs/2101.09725
Singing Voice Detection (SVD) has been an active area of research in music information retrieval (MIR). Currently, two deep neural network-based methods, one based on CNN and the other on RNN, exist in literature that learn optimized features for the
Externí odkaz:
http://arxiv.org/abs/2011.04297
Autor:
Reddy, M Kiran, Rao, K Sreenivasa
Cross-lingual voice conversion (CLVC) is a quite challenging task since the source and target speakers speak different languages. This paper proposes a CLVC framework based on bottleneck features and deep neural network (DNN). In the proposed method,
Externí odkaz:
http://arxiv.org/abs/1909.03974
The aim of this paper is to develop a flexible framework capable of automatically recognizing phonetic units present in a speech utterance of any language spoken in any mode. In this study, we considered two modes of speech: conversation, and read mo
Externí odkaz:
http://arxiv.org/abs/1908.09634