Mel Frequency Cepstral Coefficient Research Papers (original) (raw)

An investigation into the feature extraction and selection of infant cry with asphyxia is presented in this paper. The feature of the cry signal was extracted using mel frequency cepstrum coefficient (MFCC) analysis and the significant coefficients were selected using orthogonal least square (OLS) algorithm. The effect of varying the number of MFCC filter banks on the feature selection was examined. It was found that the best set of coefficients could be achieved when 40 filter banks were used.

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- Pediatrics, Signal Processing, Feature Selection, Paediatrics
- by Dr Waleed Al-Nuaimy
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- Pattern Recognition, Ground Penetrating Radar, Neural Network, Gpr
- by Amita Dev
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- Speech Recognition, Information, Feature Extraction, Spectrum

In this paper, we present an approach based on convolutional neural networks to build an automatic speech recognition system for the Amazigh language. This system is built with TensorFlow and uses mel frequency cepstral coefficient (MFCC) to extract features. In order to test the effect of the speaker's gender and age on the accuracy of the model, the system was trained and tested on several datasets. The first experiment the dataset consists of 9240 audio files. The second experiment the dataset consists of 9240 audio files distributed between females and males' speakers. The last experiment 3 the dataset consists of 13860 audio files distributed between age 9-15, age 16-30, and age 30+. The result shows that the model trained on a dataset of adult speaker's age +30 categories generates the best accuracy with 93.9%.

- by TELKOMNIKA JOURNAL and +1
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- Speech Recognition, Deep Learning, Spectrogram, Mel Frequency Cepstral Coefficient
- by Tibor Fegyo
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- Speech Recognition, Databases, Speech Technology, MFCC

We present a new approach for classifying mpeg-2 video sequences as 'cartoon' or 'non-cartoon' by analyzing specific video and audio features of consecutive frames in real-time. This is part of the well-known video-genre- classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 (12).

- by Ronald Glasberg
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- Visualization, Music Genre Classification, Feature Extraction, Digital Tv
- by winda astuti
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- Animal Behavior, Natural disaster, Feature Extraction, Support vector machine

In Islamic religion, mistakes in recitation of holy Quran (the sacred book of Muslims) are forbidden. Mistakes can be missing words, verse, misreading Harakat (pronunciations, punctuations, and accents). Thus, a hafiz/reciter who memorizes the holy Quran, needs other hafiz/tutor who listens the recitation and points oral mistakes. Due to the seriously commitment, the availability and expertise of a hafiz are also questionable. A listener can also make mistakes while hearing imputable to environmental interruptions like noise, attention. In order to tackle this issue, we designed, developed, and tested the E-hafiz system. E-hafiz is based on Mel-Frequency Cepstral Coefficient (MFCC) technique to extract voice features from Quranic verse recitation and maps them with the data collected during the training phase. Any mismatch mistake is pointed out. Testing results of short verses of Quran using the E-hafiz system are very encouraging.

- by Antonio Bonafonte and +1
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- Cognitive Science, Speech Synthesis, Speech Recognition, Linguistics
- by Kalai Selvan
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- Computational Modeling, Circuits and Systems, Speech Recognition, Hardware

In this work, Classical Turkish Music songs are classified into six makams. Makam is a modal framework for melodic development in Classical Turkish Music. The effect of the sound clip length on the system performance was also evaluated. The Mel Frequency Cepstral Coefficients (MFCC) were used as features. Obtained data were classified by using Probabilistic Neural Network. The best correct recognition ratio was obtained as 89,4% by using a clip length of 6 s.

- by sukumar dandapat
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- Cognitive Science, Speaker Recognition, Speech Production, Linguistics
- by Réda Adjoudj and +1
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- Speaker Recognition, Support Vector Machines, Databases, Speech
- by Liyanage De Silva
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- Cognitive Science, Speech Synthesis, Speech Recognition, Linguistics
- by Phillip DeLeon
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- Engineering, Speech Recognition, Speech Processing, Feature Extraction
- by Aditya Kandali
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- Cognitive Science, Natural Language Processing, Speech Recognition, Linguistics
- by J Godino-Llorente and +1
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- Pattern Recognition, Neural Network, Speaker Verification, Discriminant Analysis

Age estimation based on human's speech features is an interesting subject in Automatic Speech Recognition (ASR) systems. There are some works in literature on speaker age estimation but it needs more new works especially for Persian speakers. In age estimation, like other speech processing systems, we encounter with two main challenges: finding an appropriate procedure for feature extraction, and selecting a reliable method for pattern classification. In this paper we propose an automatic age estimation system for classification of 6 age groups of various Persian speaker people. Perceptual Linear Predictive (PLP) and Mel-Frequency Cepstral Coefficients (MFCC) are extracted as speech features and SVM is utilized for classification procedure. Furthermore the effects of variations in parameter of kernel function, time of frame length in sampling process, the number of MFCC coefficients, and the order of PLP on system efficiency has been evaluated, and the results has been compared.

- by Hassan Ezzaidi
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- FPGA, Field-Programmable Gate Arrays, Feature Extraction, Generators
- by Poonam Bansal
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- Speech Recognition, Information, Higher Order Thinking, Feature Extraction
- by Arshpreet Kaur
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- Speech Recognition, Speech Processing, Speech, Noise
- by Mihail Popescu
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- Algorithms, Acoustics, Biomedical Engineering, Sound
- by H. Hammady and +1
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- Computer Engineering, Speech Recognition, hidden Markov model, Systems

Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are

- by Md. Ali Hossain
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- Automatic Speech Recognition, Speech Recognition, ASR, hidden Markov model

Results from preliminary research on recognition of Polish birds’ species are presented in the paper. Bird voices were recorded in a highly noised municipal environment. High 96 kHz sampling frequency has been used. As a feature set standard mel-frequency cepstral coefficients (MFCC) and recently proposed human-factor cepstral coefficients (HFCC) parameters were selected. Superior performance of the HFCC features over MFCC ones has been observed. Proper limiting of the maximal frequency during HFCC feature extraction results in increasing accuracy of birds’ species recognition. Good initial results are very promising for practical application of the methods described in the paper in monitoring of protected birds’ area.

- by Robert Wielgat and +1
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- Technology, Bioacoustics, Signal Processing, Human Factors
- by Ahamad Bin
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- English language, Back Propagation, Digital Signal Processing, Speech Recognition
- by Tomi Kinnunen
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- Speaker Recognition, Speech Recognition, Speech Processing, Speech
- by Hadi Harb
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- Audio Signal Processing, Time-Frequency Analysis, Navigation, Wavelet Transforms
- by Sabri Gurbuz
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- Cognitive Science, Speech Recognition, Linguistics, Speech Communication
- by Vaishali Shinde
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- Engineering, Data Mining, Signal Processing, Speaker Recognition
- by Julian Arias
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- Mutual Information, Support vector machine, Speech Signal Processing, Spectrum
- by James Liu
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- Back Propagation, Automatic Speech Recognition, Speech Recognition, Speech Segmentation
- by Fulufhelo Nelwamondo and +1
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- Condition Monitoring, hidden Markov model, Feature Extraction, Gaussian Mixture Model
- by Sreyas Raju
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- Cognitive Science, Speech Recognition, Linguistics, Speech Communication
- by Axel Plinge
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- Speech Processing, Speech, Supervised Learning, Vectors
- by Jahangir Alam
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- Speech Recognition, Estimation Theory, Speech Processing, Speech
- by Emmanouil Benetos
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- Artificial Intelligence, Informatics, Audio Signal Processing, Speech Acoustics

Almost all current automatic speech recognition (ASR) systems conventionally append delta and double-delta cepstral features to static cepstral features. In this work we describe a modified feature-extraction procedure in which the time-difference operation is performed in the spectral domain, rather than the cepstral domain as is generally presently done. We argue that this approach based on "delta-spectral" features is needed because even though delta-cepstral features capture dynamic speech information and generally greatly improve ASR recognition accuracy, they are not robust to noise and reverberation. We support the validity of the delta-spectral approach both with observations about the modulation spectrum of speech and noise, and with objective experiments that document the benefit that the delta-spectral approach brings to a variety of currently popular feature extraction algorithms. We found that the use of delta-spectral features, rather than the more traditional delta-cepstral features, improves the effective SNR by between 5 and 8 dB for background music and white noise, and recognition accuracy in reverberant environments is improved as well.

- by chanwoo kim
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- Automatic Speech Recognition, Speech Recognition, Speech, Denoising
- by S. Vaseghi
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- Phonetics, Psychoacoustics, Speech perception, Speech Acoustics
- by Tomi Kinnunen
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- Engineering, Statistical Analysis, Speech Processing, Speaker Verification