Deep Neural Networks: A Case Study for Music Genre Classification
Deep Neural Networks: A Case Study for Music Genre Classification深度神经网络:音乐类型分类的案例研究
Music classification is a challenging problem with many applications in today’s large-scale datasets with Gigabytes of music files and associated metadata and online streaming services. Recent success with deep neural network architectures on large-scale datasets has inspired numerous studies in the machine learning community for various pattern recognition and classification tasks such as automatic speech recognition, natural language processing, audio classification and computer vision. In this paper, we explore a two-layer neural network with manifold learning techniques for music genre classification. We compare the classification accuracy rate of deep neural networks with a set of well-known learning models including support vector machines (SVM and `1-SVM), logistic regression and `1- regression in combination with hand-crafted audio features for a genre classification task on a public dataset. Our experimental results show that neural networks are comparable with classic learning models when the data is represented in a rich feature space.
音乐分类是当今海量音乐数据集和相关元数据以及在线流媒体服务中一个具有挑战性的问题。近年来，深度神经网络体系结构在大规模数据集上的成功，激发了机器学习领域的大量研究，用于各种模式识别和分类任务，如自动语音识别、自然语言处理、音频分类和计算机视觉。本文探讨了一种具有多种学习技术的双层神经网络用于音乐类型分类。我们将深度神经网络的分类准确率与一组著名的学习模型进行比较，这些学习模型包括支持向量机(SVM和1-SVM)、逻辑回归和1-回归，并结合手工制作的音频特征，用于公共数据集上的类型分类任务。我们的实验结果表明，当数据在丰富的特征空间中表示时，神经网络可以与经典学习模型相媲美。
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