Author(s)

Faishal Badsha¹, Rafiqul Islam²

¹Department of Mathematics and Statistics, Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh.
²Mathematics Discipline, Science, Engineering and Technology School, Khulna University, Khulna, Bangladesh.

Emotion is such a unique power of human trial that plays a vital role in distinguishing human civilization from others. Voice is one of the most important media of expressing emotion. We can identify many types of emotions by talking or listening to voices. This is what we know as a voice signal. Just as the way people talk is different, so is the way they express emotions. By looking or hearing a person’s way of speaking, we can easily guess his/her personality and instantaneous emotions. People’s emotion and feelings are expressed in different ways. It is through the expression of emotions and feelings that people fully express his thoughts. Happiness, sadness, and anger are the main medium of expression way of different human emotions. To express these emotions, people use body postures, facial expressions and vocalizations. Though people use a variety of means to express emotions and feelings, the easiest and most complete way to express emotion and feelings is voice signal. The subject of our study is whether we can identify the right human emotion by examining the human voice signal. By analyzing the voice signal through wavelet, we have tried to show whether the mean frequency, maximum frequency and L_p values conform to a pattern according to its different sensory types. Moreover, the technique applied here is to develop a concept using MATLAB programming, which will compare the mean frequency, maximum frequency and L_p norm to find relation and detect emotion by analyzing different voices.

MATLAB Programming, Wavelet, Haar Decomposition, Voice Signal, Mean Frequency, Maximum Frequency, L_p Norm

Badsha, F. and Islam, R. (2020) Emotion Detection by Analyzing Voice Signal Using Wavelet. American Journal of Computational Mathematics, 10, 485-502. doi: 10.4236/ajcm.2020.104028.