One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions of copyrighted commercial music. We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091 tracks and 148,826 associated playlists annotated by 30,652 different tags. All the data is gathered from Melon, a popular Korean streaming service. The dataset is suitable for music information retrieval tasks, in particular, auto-tagging and automatic playlist continuation. Even though the latter can be addressed by collaborative filtering approaches, audio provides opportunities for research on track suggestions and building systems resistant to the cold-start problem, for which we provide a baseline. Moreover, the playlists and the annotations included in the Melon Playlist Dataset make it suitable for metric learning and representation learning.

This paper presents a robust algorithm to recognize human faces efficiently. Although the principle component analysis (PCA) is one of the most popular feature extraction methods, it requires too much computational load and memory capacity to implement a real-time embedded system for face recognition. To overcome the drawback, we employ the incremental two-directional two-dimensional PCA (I(2D)2PCA) which combines (2D)2PCA to demand much less computational complexity than the conventional PCA and the incremental PCA (IPCA) to adapt the eigenspace only using a new incoming sample datum without memorizing all of the previous trained data. In addition, robustness to illumination variations is addressed by introducing the modified census transform (MCT) which is a local normalization method useful for real-world application and implementation in an embedded system. Experimental results on the Yale Face Database B demonstrate that the proposed method based on the I(2D)2PCA with MCT preprocessing provided efficient and robust face recognition.