Results 181 to 190 of about 8,304 (223)

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

The impact of the pitch on the estimation of MFCC

2011 19thTelecommunications Forum (TELFOR) Proceedings of Papers, 2011
In this paper, the impact of the pitch on the variability of MFCC, and their influence on the performance of the automatic speech recognition system, is analyzed. In case that a speaker has a high pitch, the distance between adjacent harmonics in the spectrum of voiced phonemes is larger, which results in poorer description of the spectral envelope ...
Niksa M. Jakovljevic, Branislav Z. Popovic, Marko B. Janev, Vlado D. Delic   +3 more
openaire   +1 more source

Speech Emotion Recognition Based on Improved MFCC

Proceedings of the 2nd International Conference on Computer Science and Application Engineering, 2018
Speech1 Emotion Recognition SER uses the Berlin EMO-DB database, seven emotions. Traditional emotional features and their statistics are used in SER. Two improved Mel Frequency Cepstrum Coefficients MFCC features are added to this experiment, which extract MFCC parameters from the energy curve and the fundamental frequency curve, that are energy MFCC ...
Yan Wang, Weiping Hu
openaire   +1 more source

Auditory model based modified MFCC features

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Using spectral and spectro-temporal auditory models, we develop a computationally simple feature vector based on the design architecture of existing mel frequency cepstral coefficients (MFCCs). Along with the use of an optimized static function to compress a set of filter bank energies, we propose to use a memory-based adaptive compression function to ...
Saikat Chatterjee, W. Bastiaan Kleijn
openaire   +1 more source

Speaker recognition by combining MFCC and phase information

Interspeech 2007, 2007
In conventional speaker recognition method based on MFCC, the phase information has been ignored. In this paper, we proposed a method that integrates the phase information on a speaker recognition method. The speaker identification experiments were performed using NTT database which consists of sentences uttered at normal speed mode by 35 Japanese ...
Seiichi Nakagawa, Kouhei Asakawa, Longbiao Wang   +2 more
openaire   +1 more source

Classification of Heart Sounds Using MFCC and CNN

2021
An excellent heart sound classification system can be used as a good method to complete the daily heart sound detection under the condition of low cost and high efficiency, which is convenient to detect problems in the early stage of heart disease, and at the same time can alleviate the problem of medical staff shortage.
Kai Wang, Kang Chen
openaire   +1 more source