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Mel Frequency Cepstral Coefficients and Support Vector Machines for Cough Detection
Asthma, pneumonia, chronic obstructive pulmonary disease (COPD), and most recently, the covid-19 illness all include cough as one of its most...
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Mel-Frequency Cepstral Coefficient Features Based on Standard Deviation and Principal Component Analysis for Language Identification Systems
Spoken language identification (LID) is the process of determining and classifying natural language from a given content and dataset. Data must be...
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Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient
The Covid-19 pandemic is one of the most significant global health concerns that have emerged in this decade. Intelligent healthcare technology and...
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Linear Frequency Residual Cepstral Coefficients for Speech Emotion Recognition
As technology advances, our reliance on machines grows, necessitating the development of effective approaches for Speech Emotion Recognition (SER) to...
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Spoken Language Identification Using Linear Frequency Residual Cepstral Coefficients
This paper aims to identify the spoken language of the person given the utterances using Linear Frequency Residual Cepstral Coefficients (LFRCC)....
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Generalized Nonlinear Rectification Function for Estimating Mel Cepstral Coefficients from Colombian Birdsongs
Birds monitoring is important for assessing biodiversity and other ecosystemic aspects. For this purpose, recording and analyzing of birdsongs have...
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A speaker identification-verification approach for noise-corrupted and improved speech using fusion features and a convolutional neural network
The degraded quality of the speech input signal has a negative impact on speaker recognition techniques. We address the issues of speaker recognition...
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Multilayered convolutional neural network-based auto-CODEC for audio signal denoising using mel-frequency cepstral coefficients
The denoising of audio signal and quality enhancement has a substantial contribution in speaker identification, audio transmission, hearing aids,...
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Comparison of the effectiveness of cepstral coefficients for Russian speech synthesis detection
Modern speech synthesis technologies can be used to deceive voice authentication systems, phone scams, or discredit public figures. An urgent task is...
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Modified Group Delay Features for Emotion Recognition
As technological advancements progress, dependence on machines is inevitable. Therefore, to facilitate effective interaction between humans and...
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An automatic method using MFCC features for sleep stage classification
Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds...
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Bee detection in bee hives using selective features from acoustic data
Honeybees, a key pollinator of the world’s most cultivated crops, are experiencing colony collapses due to a variety of factors. The existence of...
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MFCC in audio signal processing for voice disorder: a review
Voice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction...
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Deep Learning Algorithms for Speech Emotion Recognition with Hybrid Spectral Features
One of the popular research domains in Automatic Speech Recognition (ASR) is to identify emotions from the utterances of speech samples of human...
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Replay spoof detection for speaker verification system using magnitude-phase-instantaneous frequency and energy features
Spoofing attack detection is one of the essential components in automatic speaker verification (ASV) systems. The success of\ ASV-2015 shows a great...
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Convolution neural network based automatic speech emotion recognition using Mel-frequency Cepstrum coefficients
A significant role is played by Speech Emotion Recognition (SER) with different applications in affective computing and human-computer interface. In...
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VGGish transfer learning model for the efficient detection of payload weight of drones using Mel-spectrogram analysis
This paper presents an accurate model for predicting different payload weights from 3DR SOLO drone acoustic emission. The dataset consists of eleven...
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M-GFCC: Audio Copy-Move Forgery Detection Algorithm Based on Fused Features of MFCC and GFCC
Audio copy-move forgery has seriously affected the authenticity of audio, and copy-move forgery detection and localization of audio has become an...
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Studying the Effect of Frame-Level Concatenation of GFCC and TS-MFCC Features on Zero-Shot Children’s ASR
The work presented in this paper aims at enhancing the recognition performance of zero-shot children’s speech recognition task through frame-level...
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Time-frequency visual representation and texture features for audio applications: a comprehensive review, recent trends, and challenges
The conventional audio feature extraction methods employed in the audio analysis are categorized into time-domain and frequency-domain. Recently, a...
