Emotion recognition based on the energy distribution of plosive syllables

Abstract

<p><span lang="EN-US">We usually encounter two problems during speech emotion recognition (SER): expression and perception problems, which vary considerably between speakers, languages, and sentence pronunciation. In fact, finding an optimal system that characterizes the emotions overcoming all these differences is a promising prospect. In this perspective, we considered two emotional databases: Moroccan Arabic dialect emotional database (MADED), and Ryerson audio-visual database on emotional speech and song (RAVDESS) which present notable differences in terms of type (natural/acted), and language (Arabic/English). We proposed a detection process based on 27 acoustic features extracted from consonant-vowel (CV) syllabic units: </span><em><span lang="EN-US">\ba, \du, \ki, \ta</span></em><span lang="EN-US"> common to both databases. We tested two classification strategies: multiclass (all emotions combined: joy, sadness, neutral, anger) and binary (neutral vs. others, positive emotions (joy) vs. negative emotions (sadness, anger), sadness vs. anger). These strategies were tested three times: i) on MADED, ii) on RAVDESS, iii) on MADED and RAVDESS. The proposed method gave better recognition accuracy in the case of binary classification. The rates reach an average of 78% for the multi-class classification, 100% for neutral vs. other cases, 100% for the negative emotions (i.e. anger vs. sadness), and 96% for the positive vs. negative emotions.</span></p>

Bangla song genre recognition using artificial neural network

“…Day by day new genres are emerging daily for example nowadays people are listening to more electronic music and cover song [2]. Song genre classification and prediction plays a valuable role in various fields and industries such as recommending music based on user mood [3], emotion [4] is the important task and on the other hand applications for genre classification and prediction include music education and music therapy, where choosing music based on genre can aid in therapeutic purposes. Some research has been done for music genre classification.…”