Analyzing noise robustness of MFCC and GFCC features in speaker identification

By Xiaojia Zhao, Deliang Wang. 2013

210 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Speech & Audio, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Abdulbasit K. Al-Talabani
2. Zrar Khald Abdul

In Signal Processing

1. Abdulbasit K. Al-Talabani
2. Zrar Khald Abdul

Citations in the corpus

• Learning Robust and Multilingual Speech Representations (2020, neutral): “In contrast, standard MFCC features have been tested in terms of noise robustness and it is known that such representations are sensitive to additive noise (Zhao and Wang, 2013).”
• Mel Frequency Cepstral Coefficient and its Applications: A Review (2022, background): “The filtering process alters energy distribution across frequencies, as well as the overall energy level [8].”