A  map-based  approach,   which  treats  2-dimensional#R##N#acoustic features like image processing, has recently attracted attention in music genre classiﬁcation. While this is successful at extracting local music-patterns compared with other timbral-feature-based methods, the extracted features are not sufﬁcient for music genre classiﬁcation. In this paper, we focus on appropriate feature extraction and proper classiﬁcation by integrating the features. For the musical feature extraction, we calculate gray level co-occurrence matrix (GLCM) descriptors with different offsets from a short-term mel spectrogram.  These feature maps are integratively classiﬁed using convolutional neural network (CNN). In our experiments, we got a large improvement of more than 10 points in the classiﬁcation accuracy, compared with conventional map-based methods.#R##N#Index Terms:  music genre classiﬁcation, music infor-#R##N#mation retrieval, music feature extraction, convolutional#R##N#neural network
['Toru Nakashika', 'Christophe Garcia', 'Tetsuya Takiguchi']
Local-feature-map Integration Using Convolutional Neural Networks for Music Genre Classification.