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Music Genre and Emotion Recognition Using Both Audio and Textual Features Analysis

عنوان مقاله: Music Genre and Emotion Recognition Using Both Audio and Textual Features Analysis
شناسه ملی مقاله: ICEEC01_331
منتشر شده در کنفرانس بین المللی تحقیقات بنیادین در مهندسی برق در سال 1396
مشخصات نویسندگان مقاله:

Behnam Taheri - M.S Student, Department of Computer EngineeringWest Tehran Branch, Islamic Azad UniversityTehran, Iran
Sina Dami - Assistance Professor, Department of Computer EngineeringWest Tehran Branch, Islamic Azad UniversityTehran, Iran

خلاصه مقاله:
Audio content analysis is about summarizing features of audio and classify them. Structural analysis is about high-level things like predicting tags (Genres), recommendation systems, search for cover ID. Most of the early-stage automatic Music Emotion Recognition (MER) systems were based on audio content analysis. Later on, researchers started combining audio and lyrics, leading to bi-modal MER systems with improved accuracy. In this paper, we proposed a novel method which combines the both audio and lyrics (textual) features with LDA topic model for MER followed by a support vector machine. Experimental results showed that the proposed method is more accurate than the baselines.

کلمات کلیدی:
Music Genre Classification, Music Emotion Recognition, Audio Features, Lyrics Analysis, LDA Topic Model

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/673086/