Artificial Intelligent Systems and Machine Learning

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Applicational Areas of MFCC


Affiliations
1 Department of Computer Science, Guru Gobind Singh Indraprastha University, New Delhi, India
     

   Subscribe/Renew Journal


Mel Frequency Cepstral Coefficient is a very common and capable technique for signal processing. It is the basic method used for extracting the features of the voice signal .It is a powerful and popular acoustic vector that is used to represent and recognize the voice features and characteristics of the speaker. Mel-frequency cepstral coefficients are the coefficients that collectively represent the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. In this paper, we study about the various applications of MFCCs. The methods which were briefly studied include Vector Quantization (VQ), K Nearest Nieghbor (KNN), Dynamic Time Wrapping (DTW), Multi-Layered Perceptron (MLP), Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Hidden Markov Model.


Keywords

Mel-Frequency Cepstral Coefficient, Speech Recognition Applications, Speaker Recognition Applications, Medical Applications, Clustering Classifiers.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Manjot Kaur; Gill Reetkamal Kaur and Jagdev Kaur. Vector Quantization based Speaker Identification, International Journal of Computer Applications (0975 – 8887) Volume 4 – No.2, July 2010

  • Kshitiz Kumar; Chanwoo Kim and Richard M. Stern. DELTA-SPECTRAL CEPSTRAL COEFFICIENTS FOR ROBUST SPEECH RECOGNITION, Carnegie Mellon University, Pittsburgh

  • The MFCC, Aldebaro Klautau - 11/22/05.

  • Satya Narayana Penke; Phanindra Sai Srinivas Gudipudi; Bhanu Prakash Panchakarla; Srikanth Hemadri and Rajeev Ijjada. Frame Optimization and Speaker Recognition through Full Distance Matrix Approach, Indian Journal of Science and Technology, Vol 7(8), 1189–1195, August 2014

  • Shikha Gupta; Jafreezal Jaafar; Wan Fatimah wan Ahmad and Arpit Bansal. FEATURE EXTRACTION USING MFCC, Signal & Image Processing, An International Journal (SIPIJ) Vol.4, No.4, August 2013

  • Mehmet Cenk Sezgin; Bilge Gunsel and Gunes Karabulut Kurt. Perceptual audio features for emotion detection, Sezgin et al. , EURASIP Journal on Audio, Speech, and Music Processing 2012, 2012:16

  • Tsang-Long Pao; Yu-Te Chen; Jun-Heng Yeh; Yun-Maw Cheng and Yu-Yuan Lin. A Comparative Study of Different Weighting Schemes on KNN-Based Emotion Recognition in Mandarin Speech, Tatung University 40 ChungShan North Road, 3rd Section Taipei 104, Taiwan

  • Syed Ayaz Ali Shah; Azzam ul Asar and Syed Waqar Shah. Interactive Voice Response with Pattern Recognition Based on Artificial Neural Network Approach, NWFP University of Engineering and Technology, Peshawar, Pakistan

  • Prof. M. R. Dixit. Learning Assistant in Educational Field Using Automatic Speech Recognition, International Journal of Innovative Research in Computer Science & Technology (IJIRCST) ISSN: 2347-5552, Volume-1, Issue-2, November- 2013

  • Anjali Bala; Abhijeet Kumar and Nidhika Birla.VOICE COMMAND RECOGNITION SYSTEM BASED ON MFCC AND DTW, International Journal of Engineering Science and Technology

  • Sharma P.K.; Lakshmikantha B.R. and Sundar K.S. . Real time control of DC motor drive using speech recognition, Power Electronics (IICPE), 2010 India International Conference

  • Aruna, C. ; Dhivya Parameswari, A. ; Malini, M. and Gopu G. . Voice recognition and touch screen control based wheel chair for paraplegic persons, Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference

  • Hamid Behravan. Dialect and Accent Recognition, University of Eastern Finland

  • Ali Zulfiqar1; Aslam Muhammad and Martinez Enriquez A. M. A Speaker Identification System using MFCC Features with VQ Technique, 2009 Third International Symposium on Intelligent Information Technology Application

  • Laura E. Boucheron and Phillip L. De Leon. On the Inversion of Mel-Frequency CepstralCoefficients for Speech Enhancement Applications, Klipsch School of Electrical and Computer Engineering New Mexico State University

  • J. I. Godino-Llorente* and P. Gómez-Vilda. Automatic Detection of Voice Impairments by Means of Short-Term Cepstral Parameters and Neural Network Based DETECTORS, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 51, NO. 2, FEBRUARY 2004

  • Francesco Beritelli and Andrea Spadaccini. HEART SOUNDS QUALITY ANALYSIS FOR AUTOMATIC CARDIAC BIOMETRY APPLICATIONS, University of Catania, Italy

  • Davood MAHMOUD; Hossein Marvi; Mehdi Taghizadeh; Ali Soleimani; Farbod Razzazi and Marzieh Mahmoodi. Age Estimation Based on Speech Features and Support Vector Machine, 2011 3rd Computer Science and Electronic Engineering Conference

  • Arijit Ghosal; Rudrasis Chakraborty; Bibhas Chandra Dhara and Sanjoy Kumar Saha. Music Classification based on MFCC Variants and Amplitude Variation Pattern: A Hierarchical Approach, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 5, No. 1, March, 2012

  • David Pye. Content-Based Methods for the Management of Digital Music, AT&T Laboratories Cambridge,Cambridge, UK

  • Róisín Loughran; Jacqueline Walker; Michael O’Neill and Marion O’Farrell. The Use of Mel-frequency Cepstral Coefficients in Musical Instrument Identification, University of Limerick, Limerick, Ireland

  • Rajesh M. Hegde and Hema A. Murthy. Automatic language Identification and discrimination using the modified group delay feature, Department of Computer Science and Engineering Indian Institute of Technology,Chennai, India

  • Tripti Kapoor and R.K. Sharma. Parkinson’s disease Diagnosis using Mel-frequency Cepstral Coefficients and Vector Quantization, International Journal of Computer Applications (0975–8887) Volume14–No.3, January2011


Abstract Views: 283

PDF Views: 5




  • Applicational Areas of MFCC

Abstract Views: 283  |  PDF Views: 5

Authors

Preeti Kapoor
Department of Computer Science, Guru Gobind Singh Indraprastha University, New Delhi, India
Narina Thakur
Department of Computer Science, Guru Gobind Singh Indraprastha University, New Delhi, India

Abstract


Mel Frequency Cepstral Coefficient is a very common and capable technique for signal processing. It is the basic method used for extracting the features of the voice signal .It is a powerful and popular acoustic vector that is used to represent and recognize the voice features and characteristics of the speaker. Mel-frequency cepstral coefficients are the coefficients that collectively represent the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. In this paper, we study about the various applications of MFCCs. The methods which were briefly studied include Vector Quantization (VQ), K Nearest Nieghbor (KNN), Dynamic Time Wrapping (DTW), Multi-Layered Perceptron (MLP), Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Hidden Markov Model.


Keywords


Mel-Frequency Cepstral Coefficient, Speech Recognition Applications, Speaker Recognition Applications, Medical Applications, Clustering Classifiers.

References