Improved MFCC and LPC algorithm for bundelkhandi isolated digit speech recognition

Dixit, Abhishek; Vidwans, Abhinav; Sharma, Pankaj

doi:10.1109/iceeot.2016.7755413

Cited by 19 publications

(5 citation statements)

References 4 publications

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“…A fundamental requirement in obtaining the output of the Mel filter bank is its proper configuration, ensuring its adaptation to the context of our problem. These filters are commonly constructed in the frequency domain, and central frequencies are usually distributed uniformly along the frequency axis (Dixit et al 2016;Rao and Manjunath 2017). The selection of filter bank parameters, including the number of filters, their center frequencies, and bandwidths, directly influences the coverage and resolution characteristics of the Mel filter bank.…”

Section: Mel Filter Bank Projectmentioning

confidence: 99%

“…For comparative purposes, MFCCs were extracted in two distinct ways, both using 42 Mel filters. The first approach followed the conventional distribution of the Mel filter bank, as described in Liu et al (2014), Dixit et al (2016), Rao and Manjunath (2017) and referred to as MFCC. The second approach, denoted as MFCC † , utilizes the Mel filter configuration detailed in Section 2.4.…”

Section: Table 2 Data Set With Divisions Into Classesmentioning

confidence: 99%

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Improved spectral dynamic features extracted from audio data for classification of marine vessels

de Brito Santos,

de Moraes Calazan

2024

Intell. Mar. Technol. Syst.

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Underwater sound classification presents a unique challenge due to the complex propagation characteristics of sound in water, including absorption, scattering, and refraction. These complexities can distort and alter spectral features, hindering the effectiveness of traditional feature extraction methods for vessel classification. To address this challenge, this study proposes a novel feature extraction method that combines Mel-frequency cepstral coefficients (MFCCs) with a spectral dynamic feature (SDF) vector. MFCCs capture the spectral content of the audio signal, whereas SDF provides information on the temporal dynamics of spectral features. This combined approach aims to achieve a more comprehensive representation of underwater vessel sounds, potentially leading to improved classification accuracy. Validation with real-world underwater audio recordings demonstrated the effectiveness of the proposed method. Results indicated an improvement of up to 94.68% in classification accuracy when combining SDF with several classical extractors evaluated. This finding highlights the potential of SDF in overcoming the challenges associated with underwater sound classification.

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Section: Mel Filter Bank Projectmentioning

confidence: 99%

Section: Table 2 Data Set With Divisions Into Classesmentioning

confidence: 99%

Improved spectral dynamic features extracted from audio data for classification of marine vessels

de Brito Santos,

de Moraes Calazan

2024

Intell. Mar. Technol. Syst.

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“…In this subprocess, each frame will be changed from the time domain to the frequency domain. This process will produce a frequency spectrum that is used to simplify computation and analysis [21].…”

Section: Fft (Fast Fourier Transform)mentioning

confidence: 99%

Speech Recognition Implementation Using MFCC and DTW Algorithm for Home Automation

Haq¹,

Nasrun²,

Setianingsih³

et al. 2020

EECSI

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The use of speech recognition as part of home automation, especially for smart homes, is an exciting thing that is still being developed. That is because of human needs for comfort, convenience, quality of life, and better safety. Speech recognition built in this study is used as a device to control smart home devices by identifying the commands spoken by users, especially in a state of clean speech. The command used is a predetermined consecutive word. For the extraction of voice commands, the MFCC algorithm is used to match spoken words with templates using the Dynamic Time Warping (DTW) algorithm. DTW algorithm can find the difference between 2time series that have different lengths of time. The results of the accuracy of this system by using these algorithms were successfully carried out by 86.67%, with an average time required to identify the commands of 5.28 seconds.

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“…Linear predictive coding (LPC) is a digital method for encoding an analog signal [10]. The way LPC works is that it predicts the next value of a signal based on the information it has received in the past, forming a linear pattern.…”

Section: Linear Predictive Coding Coefficients (Lpcc)mentioning

confidence: 99%

A Review on Emotion Recognition Algorithms using Speech Analysis

Gunawan

Alghifari

Morshidi

et al. 2018

IJEEI

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In recent years, there is a growing interest in speech emotion recognition (SER) by analyzing input speech. SER can be considered as simply pattern recognition task which includes features extraction, classifier, and speech emotion database. The objective of this paper is to provide a comprehensive review on various literature available on SER. Several audio features are available, including linear predictive coding coefficients (LPCC), Melfrequency cepstral coefficients (MFCC), and Teager energy based features. While for classifier, many algorithms are available including hidden Markov model (HMM), Gaussian mixture model (GMM), vector quantization (VQ), artificial neural networks (ANN), and deep neural networks (DNN). In this paper, we also reviewed various speech emotion database. Finally, recent related works on SER using DNN will be discussed.

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Improved MFCC and LPC algorithm for bundelkhandi isolated digit speech recognition

Cited by 19 publications

References 4 publications

Improved spectral dynamic features extracted from audio data for classification of marine vessels

Improved spectral dynamic features extracted from audio data for classification of marine vessels

Speech Recognition Implementation Using MFCC and DTW Algorithm for Home Automation

A Review on Emotion Recognition Algorithms using Speech Analysis

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