Biomedical Diagnosis of Infant Cry Signal Based on Analysis of Cepstrum by Deep Feedforward Artificial Neural Networks
Salim Lahmiri, Chakib Tadj, Christian Gargour
Abstract
The automatic analysis and detection of audio signals is an important field of research with promising applications in various biomedical engineering problems such as speech, heart murmur, and lung sound analysis and classification. In this regard, automatic classification of infant vocalizations is becoming an appealing research area for medical diagnosis in clinical milieu. Indeed, the analysis and classification of infant cry records is a conventional non-inva-sive technique to distinguish between healthy and unhealthy infants.
Topics & Concepts
CepstrumComputer scienceArtificial neural networkSpeech recognitionArtificial intelligenceMel-frequency cepstrumSIGNAL (programming language)Field (mathematics)Feedforward neural networkClinical diagnosisSignal processingPattern recognition (psychology)Machine learningFeature extractionDigital signal processingMedicineClinical psychologyMathematicsPure mathematicsProgramming languageComputer hardwareInfant Health and DevelopmentSpeech and Audio ProcessingPhonocardiography and Auscultation Techniques