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Emotion recognition and emotion based classification of audio using genetic algorithm - an optimized approach

机译：基于遗传算法的情绪识别和基于情绪的音频分类-一种优化方法

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摘要

Music information retrieval (MIR) is one of the vast areas of research and it is gaining more and more attention from researchers, as well as from the music developing community. Music can be classified throughout many dimensions such as genre, mood, instrument, artists, etc. Emotion based (Mood-based) music classification is also carried out by researchers for understanding Physiological and Psychological effects of music on human mood and body. There are number of applications of Music classification such as Audio finger printing, copyright monitoring, etc. This paper explains an optimized approach for emotion detection from audio and classifies it among eight emotions. Also, provides an overview of popular algorithms, models and various techniques involved in mood-based music classification. Different mood-based music classification methods are compared with each other and their relative advantages and disadvantages are discussed. Arousal-Valence method for emotion recognition is used for music emotion detection. Some pitfalls and limitations of the existing systems are investigated. Then, a model is proposed for optimal music classification based on mood / emotion. Basically, it tries to optimize the current system by overcoming some of its short falls, such as, high computation time and low accuracy. Here, genetic algorithm is used for optimal feature selection. Thus, the average computation time for classification is reduced for large dataset.
机译：音乐信息检索（MIR）是广泛的研究领域之一，它越来越受到研究人员以及音乐开发社区的关注。音乐可以在很多方面进行分类，例如流派，情绪，乐器，艺术家等。基于情感（基于情绪）的音乐分类也由研究人员进行，以了解音乐对人类情绪和身体的生理和心理影响。音乐分类的应用非常广泛，例如音频指纹，版权监控等。本文介绍了一种用于从音频进行情感检测的优化方法，并将其分类为八个情感。此外，还概述了基于情绪的音乐分类中涉及的流行算法，模型和各种技术。比较了基于情绪的不同音乐分类方法，并讨论了它们的相对优缺点。用于情感识别的Arousal-Valence方法用于音乐情感检测。研究了现有系统的一些缺陷和局限性。然后，提出了一种基于情绪/情感的最佳音乐分类模型。基本上，它会尝试通过克服一些缺点（例如计算时间长和准确性低）来优化当前系统。在这里，遗传算法用于最佳特征选择。因此，对于大型数据集，用于分类的平均计算时间减少了。

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