Results 221 to 230 of about 3,633,480 (246)

On the inversion of Mel-frequency cepstral coefficients for speech enhancement applications

2008 International Conference on Signals and Electronic Systems, 2008
The use of Mel-frequency cepstral coefficients (MFCCs) is well established in the fields of speech processing, particularly for speaker modeling within a Gaussian mixture model (GMM) speaker recognition system. The use of GMMs for speech enhancement applications has only recently been proposed in the literature; the concept of direct inversion of the ...
Laura E. Boucheron, Phillip L. De Leon
openaire   +1 more source

Mel Frequency Cepstral Coefficients Based Similar Albanian Phonemes Recognition

2016
In Albanian language there are several phonemes that are similar in pronunciation like /q/ - /c/, /rr/ - /r/, /th/ - /dh/ and /gj/ - /xh/. These phonemes are difficult to distinguish by human ear even for native speaking Albanians from different regions.
Bertan Karahoda, Krenare Pireva, Ali Shariq Imran   +2 more
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Mel-frequency cepstral coefficients as features for automatic speaker recognition

2015 23rd Telecommunications Forum Telfor (TELFOR), 2015
Automatic speaker recognizer can be based on the use of mel-frequency cepstral coefficients as speaker features. Mel-frequency cepstral coefficients depend on energy inside considered auditory critical bands. These auditory critical bands model masking phenomena.
Ivan D. Jokic, Stevan D. Jokic, Vlado D. Delic, Zoran H. Perie   +3 more
openaire   +1 more source

Cardiac Sound Classification Using Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN)

2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), 2018
Auscultation of heart sounds is usually used as an important way to identify symptoms of heart disease. In this case the experts need to concentrate on diagnosing heart sound abnormalities in humans.
M. Rahmandani, H. A. Nugroho, N. A. Setiawan   +2 more
semanticscholar   +1 more source

Identification of Regional Dialects Using Mel Frequency Cepstral Coefficients (MFCCs) and Neural Network

2018 International Seminar on Application for Technology of Information and Communication, 2018
Dialects may affect the voice recognition because they have an influence on the intonation and the pronunciation of a syllable. Every dialect shows the characteristic of a particular tribe and region.
Ayu Mawadda Warohma, Puspa Kurniasari, Suci Dwijayanti, Irmawan, Bhakti Yudho Suprapto   +4 more
semanticscholar   +1 more source

Multiple time resolutions for derivatives of Mel-frequency cepstral coefficients

IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01., 2005
Most speech recognition systems are based on Mel-frequency cepstral coefficients and their first- and second-order derivatives. The derivatives are normally approximated by fitting a linear regression line to a fixed-length segment of consecutive frames. The time resolution and smoothness of the estimated derivative depends on the length of the segment.
G. Stemmer, C. Hacker, E. Noth, H. Niemann   +3 more
openaire   +1 more source

Fusion of mel and gammatone frequency cepstral coefficients for speech emotion recognition using deep C-RNN

International Journal of Speech Technology, 2021
U. Kumaran, S. RadhaRamMohan, Senthil Murugan Nagarajan, A. Prathik   +3 more
semanticscholar   +1 more source

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

Multimedia tools and applications, 2021
A. Siam, A. A. Elazm, Nirmeen A. El-Bahnasawy, G. Banby, F. El-Samie   +4 more
semanticscholar   +1 more source

Investigation on new Mel frequency cepstral coefficients features and hyper-parameters tuning technique for bee sound recognition

Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2022
Thi-Thu-Hong Phan, Dong Nguyen-Doan, D. Nguyen-Huu, Hanh Nguyen-Van, Thai Pham-Hong   +4 more
semanticscholar   +1 more source

Automatic Parkinson's disease detection based on the combination of long-term acoustic features and Mel frequency cepstral coefficients (MFCC)

Biomedical Signal Processing and Control, 2022
Sara Hawi, Jana Alhozami, Raneem Alqahtani, Dannah AlSafran, Maram Alqarni, Lola El Sahmarany   +5 more
semanticscholar   +1 more source