A comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequency Cepstral Coefficients (IMFCC) features for an Automatic Bird Species Recognition System

被引：0
|
作者：
Pedroza Ramirez, Angel David [1 ]
de la Rosa Vargas, Jose Ismael [1 ]
Rosas Valdez, Rogelio [1 ]
Becerra, Aldonso [1 ]
机构：
[1] Univ Autonoma Zacatecas, Unidad Acad Ingn Elect, Zacatecas, Mexico
来源：
2018 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI) | 2018年
关键词：
bioacoustics; bird classification; HMM; IMFCC; MFCC;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequency Cepstral Coefficients (IMFCC) features for an automatic bird species recognition system is proposed with the aim to validate IMFCC as a feature that can also be extracted for bird species recognition. In biodiversity monitoring task there are some traditional techniques and, bioacoustics studies biodiversity by a noninvasive way based on the relationship between animal species and its sounds. Bioacoustics methodology for avian conservation are based on automatic speech recognition techniques and one of the traditional extracted features in this area are MFCC. Nevertheless some new studies uses IMFCC as a complementary frequency information. From results, it is concluded that IMFCC features have better performance than traditional MFCC features but, performance still depends on the recognized bird sound.
引用
收藏
页数：4
相关论文
共 50 条
  • [41] RETRACTED: Breathing site classification via joint mel frequency cepstral coefficients and gammatone frequency cepstral coefficients approach (Retracted Article)
    Zhang, Jiarui
    Ling, Bingo Wing-Kuen
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2024, 46 (02) : 3623 - 3634
  • [42] Classification of Heart Sounds using Linear Prediction Coefficients and Mel-Frequency Cepstral Coefficients as Acoustic Features
    Narvaez, Pedro
    Vera, Katerine
    Bedoya, Nhikolas
    Percybrooks, Winston S.
    2017 IEEE COLOMBIAN CONFERENCE ON COMMUNICATIONS AND COMPUTING (COLCOM), 2017,
  • [43] Speaker independent phoneme recognition based on fractal dimension (DF) and the mel-frequency cepstral coefficients features
    Fekkai, S
    Al-Akaidi, M
    Blackledge, JM
    2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS: VOL I: SPEECH PROCESSING 1; VOL II: SPEECH PROCESSING 2 IND TECHNOL TRACK DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS NEURALNETWORKS FOR SIGNAL PROCESSING; VOL III: IMAGE & MULTIDIMENSIONAL SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING - VOL IV: SIGNAL PROCESSING FOR COMMUNICATIONS; VOL V: SIGNAL PROCESSING EDUCATION SENSOR ARRAY & MULTICHANNEL SIGNAL PROCESSING AUDIO & ELECTROACOUSTICS; VOL VI: SIGNAL PROCESSING THEORY & METHODS STUDENT FORUM, 2001, : 4014 - 4014
  • [44] Text independent speaker recognition using the Mel frequency cepstral coefficients and a neural network classifier
    Seddik, H
    Rahmouni, A
    Sayadi, M
    ISCCSP : 2004 FIRST INTERNATIONAL SYMPOSIUM ON CONTROL, COMMUNICATIONS AND SIGNAL PROCESSING, 2004, : 631 - 634
  • [45] Mel, Linear, and Antimel Frequency Cepstral Coefficients in Broad Phonetic Regions for Telephone Speaker Recognition
    Lei, Howard
    Lopez, Eduardo
    INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5, 2009, : 2307 - 2310
  • [46] The Recognition of Hijaiyah Letter Pronunciation Using Mel Frequency Cepstral Coefficients and Hidden Markov Model
    Fauzi, Rifan Muhamad
    Adiwijaya
    Maharani, Warih
    ADVANCED SCIENCE LETTERS, 2016, 22 (08) : 2043 - 2046
  • [47] Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)
    Ghaffar, Muhammad Saad Bin Abdul
    Khan, Umar S.
    Iqbal, J.
    Rashid, Nasir
    Hamza, Amir
    Qureshi, Waqar S.
    Tiwana, Mohsin I.
    Izhar, U.
    INFRARED PHYSICS & TECHNOLOGY, 2021, 112
  • [48] AutoVAT: An Automated Visual Acuity Recognition with Mel Frequency Cepstral Coefficients and Convolutional Neural Network
    Taufik, Derryl
    Hanafiah, Novita
    5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND COMPUTATIONAL INTELLIGENCE 2020, 2021, 179 : 458 - 467
  • [49] Material recognition for fault diagnosis in machine tools using improved Mel Frequency Cepstral Coefficients
    Yuan, Jianjian
    Li, Lin
    Shao, Hua
    Han, Muyue
    Huang, Hongcheng
    JOURNAL OF MANUFACTURING PROCESSES, 2023, 98 : 67 - 79
  • [50] MUSICAL INSTRUMENT IDENTIFICATION USING MULTISCALE MEL-FREQUENCY CEPSTRAL COEFFICIENTS
    Sturm, Bob L.
    Morvidone, Marcela
    Daudet, Laurent
    18TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO-2010), 2010, : 477 - 481
12345