Analyze Features Extraction for Audio Signal with Six Emotions Expressions
Salwa A. Al-agha1, Hilal H. Saleh2, Rana F. Ghani3
1Salwa A. Alaghais, student, Department of Electrical Engineering, and a postgraduate, University of Technology, Jaipur (R.J), India.
2Hilal H. Saleh, Department of Electrical Engineering, and a postgraduate, University of Technology, Jaipur (R.J), India.
3Rana F. Ghani, Department of Electrical Engineering, and a postgraduate, University of Technology, Jaipur (R.J), India.
Manuscript received on 15 August 2015 | Revised Manuscript received on 25 August 2015 | Manuscript Published on 30 August 2015 | PP: 38-41 | Volume-4 Issue-6, August 2015 | Retrieval Number: A5486108118/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Audio feature extraction plays an important role in analyzing and characterizing audio content. Auditory scene analysis, content-based retrieval, indexing, and fingerprinting of audio are few of the applications that require efficient feature extraction. The key to extract strong features that characterize the complex nature of audio signals is to identify their discriminatory subspaces. The audio information analysis for emotion recognition generally comprises linguistic and paralinguistic measurements. The linguistic measurement conforms to the rules of the language whereas paralinguistic measurement is the meta-data; i.e. related to how the words are spoken based on variations of pitch, intensity and spectral properties of the audio signal. This paper presents a technique for analyzing the features which extracted from recording audio signals in time domain and frequency domain by using statistical methods.
Keywords: Audio Signals, Audio Feature Analysis, Feature Extraction, Emotion Expression, MFCC, Pitch Extraction
Scope of the Article: Predictive Analysis