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Development of Optimal Feature Selection and Deep Learning Toward Hungry Stomach Detection Using Audio Signals

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Abstract

The process of transmitting signals to the body regarding the hungry stomach is referred to as the migrating motor complex (MMC) process. The intestines and stomach are considered for sensing the unavailability of food in the body. Hence, the receptors present in the stomach wall generate the electrical activity waves and activate the hunger. In general, audio signal processing algorithms include signal analysis, property extraction, and behavior prediction, identifying the pattern available in the signal, and predicting how a specific signal is correlated to various identical signals. The major challenge here is to consider the audio signals that are produced from the stomach for identifying the growling sound that well describes the hungry. The main intent of this paper is to develop an intelligent model using the deep learning concept for recognizing the hungry stomach by using the synthetically collected audio signals through mobile phones. This makes society reaching the hungry stomach by way of intelligent technology. The proposed detection model covers different phases for automated hungry stomach detection. The data acquisition is performed by gathering information using mobile phones. Further, the pre-processing of the signals is done by the median filtering and the smoothening methods. In order to perform the proper classification, the spectral features like spectral centroid, spectral roll-off, spectral skewness, spectral kurtosis, spectral slope, spectral crest factor, and spectral flux, and cepstral domain features like mel-frequency cepstral coefficients (MFCCs), linear prediction cepstral coefficients (LPCCs), Perceptual linear prediction (PLP) cepstral coefficients, Greenwood function cepstral coefficients (GFCC), and gammatone cepstral coefficients (GTCCs) are extracted. Further, the optimal feature selection is done by the improved meta-heuristic algorithm called best and worst position updated deer hunting optimization algorithm (BWP-DHOA). An improved deep learning model called optimized recurrent neural network (RNN) is used for classifying the optimal features of the audio signal into growling sound and burp sound. Finally, the performance comparison over the existing models proves the efficiency and reliability of the proposed model.

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Abbreviations

MMC:

Migrating motor complex

LPCC:

Linear prediction cepstral coefficient

ASC:

Acoustic scene classification

NN:

Nearest neighbor

MFCC:

Mel-frequency cepstral coefficient

CASA:

Computational auditory scene analysis

SSA:

Spectral similarity-based algorithm

PLP:

Perceptual linear prediction cepstral coefficient

RNN:

Recurrent neural network

MIB:

Monitoring of ingestive behavior

DNN:

Deep neural network

PPG:

Photoplethysmography

GMM:

Gaussian mixture model

DT:

Decision Tree

GFCC:

Greenwood Function Cepstral Coefficient

SVDD:

Support Vector Data Description

AED:

Audio Event Detection

PCG:

Phono-cardio grams

SVM:

Support vector machine

HS:

Heart Sounds

DCASE:

Detection and classification of acoustic scenes and events

GRU:

Gated recurrent unit

GTCC:

Gamma tone cepstral coefficient

ARMA:

Auto-regressive moving average

PSD:

Power spectral density

WSVM:

Weighted support vector machine

BWP-DHOA:

Best and worst position updated deer hunting optimization algorithm

LSTM:

Long short-term memory

MLP:

Multi-layer perceptron

ANN:

Artificial neural network

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Authors and Affiliations

  1. Department of Computer Science, Immaculate College for Women, No.4, Bharathi Road, Cuddalore, Tamil Nadu, 607001, India

    A. Maria

  2. Electronics and Communication Engineering, Jeppiaar Institute of Technology, Sriperumbudur, Tamil Nadu, India

    A. Sengol Jeyaseelan

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  1. A. Maria
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Maria, A., Jeyaseelan, A.S. Development of Optimal Feature Selection and Deep Learning Toward Hungry Stomach Detection Using Audio Signals. J Control Autom Electr Syst 32, 853–874 (2021). https://doi.org/10.1007/s40313-021-00727-8

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  • DOI: https://doi.org/10.1007/s40313-021-00727-8

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