Results 141 to 150 of about 1,316 (181)

Molecular basis for methylation-sensitive editing by Cas9. [PDF]

Nature
Roth MO, Shu Y, Zhao Y, Trasanidou D, Hoffman RD, Südfeld C, Bouzetos E, Trasanidis N, Zawrotny M, Gelasco MK, Medina ML, Das A, Rai J, Goswami HN, Wang B, van der Oost J, Li H.   +16 more
europepmc   +1 more source

Effect of the DASH diet on the sodium-chloride cotransporter and aquaporin-2 in urinary extracellular vesicles.

Am J Physiol Renal Physiol
Bielopolski D, Musante L, Hoorn EJ, Molina H, Barrows D, Carrol TS, Harding MA, Upson S, Qureshi A, Weder MM, Tobin JN, Kost RG, Erdbrügger U.   +12 more
europepmc   +1 more source

KS-WNK1 is required for the renal response to extreme changes in potassium intake.

Am J Physiol Renal Physiol
Bahena-Lopez JP, Vergara L, de la-Peña V, Gutierrez-Gallardo MA, López-Ibargüen P, García JA, Carbajal-Contreras H, Vázquez N, Rincón-Heredia R, Masso F, Bobadilla NA, Castañeda-Bueno M, Ellison DH, Gamba G, Chávez-Canales M.   +14 more
europepmc   +1 more source

Deletion of KS-WNK1 promotes NCC activation by increasing WNK1/4 abundance.

Am J Physiol Renal Physiol
Ferdaus MZ, Terker AS, Koumangoye RB, Al-Qusairi L, Welling PA, Delpire E.   +5 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Improved multitaper PNCC feature for robust speaker verification

The 9th International Symposium on Chinese Spoken Language Processing, 2014
A major challenge for practical speaker verification is the significant performance degradation in noisy circumstances. To solve the problem, a novel feature is proposed in this paper based on power normalized cepstral coefficients (PNCC). In the new feature, multitaper method and compressive gammachirp filter-bank (cGCFB) are introduced.
Liang He
exaly   +2 more sources

Combined PNCC feature extractor for robust speech recognition

2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), 2014
Recently, two major types of Power-Normalized Cepstral Coefficients (PNCCs) were proposed as noise robust Automatic Speech Recognition (ASR) front-end. All the literatures for these two PNCCs assume clean training data and clean or noisy test data. However, we find that one PNCC method has good performance for the clean training/noisy test scenario ...
Stephen A Zahorian
exaly   +2 more sources

A Comparable Study on PNCC in Speaker Diarization for Meetings

2010 First ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, 2010
In speaker diarization, the most commonly used speaker feature is MFCC, which is also most commonly used speech feature in speech recognition. The newly proposed Power Normalized Cepstrum Coefficients (PNCC) achieves impressive improvement in noisy speech recognition compare to MFCC. It consequently expects a proof for speaker diarization use.
Qiao Li, Qing Fan, Yunpeng Xiao, Weiping Ye   +3 more
exaly   +2 more sources

Study of fusion strategies and exploiting the combination of MFCC and PNCC features for robust biometric speaker identification [PDF]

2016 4th International Conference on Biometrics and Forensics (IWBF), 2016
In this paper, a new combination of features and normalization methods is investigated for robust biometric speaker identification. Mel Frequency Cepstral Coefficients (MFCC) are efficient for speaker identification in clean speech while Power Normalized Cepstral Coefficients (PNCC) features are robust for noisy environments.
Musab T. S. Al-Kaltakchi, Wai Lok Woo, Satnam Singh Dlay, Jonathon A. Chambers   +3 more
openaire   +2 more sources

ELM speaker identification for limited dataset using multitaper based MFCC and PNCC features with fusion score

Multimedia Tools and Applications, 2020
In current scenario, speaker recognition under noisy condition is the major challenging task in the area of speech processing. Due to noise environment there is a significant degradation in the system performance. The major aim of the proposed work is to identify the speaker’s under clean and noise background using limited dataset.
Rajesh Kumar M, Bharath K P, M Rajesh Kumar   +2 more
exaly   +2 more sources

Review of Modgdf & PNCC Techniques for Features Extraction in Speech Recognition

2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019
In speech recognition system, voice is the main research object. By extracting the features and classifying the speech signal, it will easy to convert voice into text. There are some factors which are affected on accuracy of input signal namely noise, language variability, domain and signal processing as well as on person’s language. In this system, we
Abdulmalik Alasadi, Ratnadeep Deshmukh
exaly   +2 more sources