Results 161 to 170 of about 3,617,625 (210)

A Smartphone-Based Acoustic Machine Learning Pipeline for Detecting Suicidal Ideation: Case-Control Model Development and Validation Study. [PDF]

open access: yesJMIR Form Res
Lyu M   +13 more
europepmc   +1 more source

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

open access: yes2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2018
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.
Aldonso Becerra
exaly   +4 more sources

Identification of Language using Mel-Frequency Cepstral Coefficients (MFCC) [PDF]

open access: yesProcedia Engineering, 2012
AbstractThis paper focuses on the task of identifying a language from speech signal. In this paper, we have use Mel-frequency cepstral coefficient as features. Language identification models are developed for fifteen Indian languages namely Assamese, Bangla, Guajarati, Hindi, Kannada, Kashmiri, Malayalam, Marathi, Nepali, Oriya, Punjabi, Rajasthani ...
Shashidhar G Koolagudi
exaly   +3 more sources

Drive-by bridge damage detection using Mel-frequency cepstral coefficients and support vector machine

open access: yesStructural Health Monitoring, 2023
Bridge damage detection using vibration data has been confirmed as a promising approach. Compared to the traditional method that typically needs to install sensors or systems directly on bridges, the drive-by bridge damage detection method has gained ...
Weiwei Lin, Zhenkun Li, Youqi Zhang
exaly   +2 more sources

Computing Mel-frequency cepstral coefficients on the power spectrum

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
We present a method to derive Mel-frequency cepstral coefficients directly from the power spectrum of a speech signal. We show that omitting the filterbank in signal analysis does not affect the word error rate. The presented approach simplifies the speech recognizers front end by merging subsequent signal analysis steps into a single one.
Sirko Molau   +3 more
openaire   +2 more sources

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