Results 131 to 140 of about 3,813 (182)

Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds. [PDF]

Sci Rep
Shatat GAE, Moustafa HE, Saraya MS, Marzouk AA, Hekal AA.   +4 more
europepmc   +1 more source

Sibilant Representation Using MFCCs and GMMs

Sibilant Representation Using MFCCs and GMMs
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Classification of speech dysfluencies with MFCC and LPCC features

Expert Systems With Applications, 2012
The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech samples from UCLASS are used for our analysis. The stuttered events are identified through manual segmentation and used for feature
Sazali Yaacob, M Hariharan
exaly   +2 more sources

Chip design of MFCC extraction for speech recognition

The Integration VLSI Journal, 2002
Summary: The Mel Frequency Cepstral Coefficient (MFCC) is one of the most important features required among various kinds of speech applications. In this paper, the first chip for speech features extraction based on MFCC algorithm is proposed. The chip is implemented as an intellectual property, which is suitable to be adopted in a speech recognition ...
Jia-Ching Wang
exaly   +2 more sources

Comparison of different implementations of MFCC

Journal of Computer Science and Technology, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thomas Fang Zheng, Guoliang Zhang, Zhanjiang Song   +2 more
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DNN based Acoustic Scene Classification using Score Fusion of MFCC and Inverse MFCC

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), 2018
Herein, we propose an Acoustic Scene Classification (ASC) based on Deep Neural Networks (DNN). The design of Mel-filer bank helps in capturing the acoustic scene characteristics in the low-frequency regions during MFCC extraction. In this paper, inverse MFCC are used as interdependent to structure of Mel filter bank.
Chandrasekhar Paseddula, Suryakanth V. Gangashetty   +1 more
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An MFCC-Based Speaker Identification System

2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), 2017
Nowadays, many speech recognition applications have been used by people in the world. Typical examples are the SIRI of iPhone, Google speech recognition system, and mobile phones operated by voice, etc. On the contrary, speaker identification in its current stage is relatively immature.
Fang-Yie Leu, Guan-Liang Lin
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Optimized MFCC feature extraction on GPU

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
In this paper, we update our previous research for Mel-Frequency Cepstral Coefficient (MFCC) feature extraction [1] and describe the optimizations required for improving throughput on the Graphics Processing Units (GPU). We not only demonstrate that the feature extraction process is suitable for GPUs and a substantial reduction in computation time can ...
Haofeng Kou, Weijia Shang, Ian R. Lane, Jike Chong   +3 more
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Phonocardiogram classification based on MFCC extraction

2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2017
In this work, a simple method for separation between normal and abnormal heart sounds (Phonocardiogram) is presented. Mel-Frequency Cepstral Coefficients (MFCC) are extracted from two different datasets of heartbeats. Several Classifiers, such as, Support Vectors Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB), Classification Tree (CT) and ...
Othmane El Badlaoui, Ahmed Hammouch
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Keyword Recognition Based on MFCC

Advanced Materials Research, 2014
After about 50 years of development, speech recognition technology has been able to achieve large vocabulary, non-specific human continuous speech recognition system. On account of Chinese pronunciation features, we research the small vocabulary, non-specific Chinese speech recognition based on continuous Hidden Markov Model approach.
Sha Yang, Tian Hu, Yun Lu Zhang
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