The basic role of DJs is creating a seamless sequence of music tracks. In order to make the DJ mix a single continuous audio stream, DJs control various audio effects on a DJ mixer system particularly in the transition region between one track and the next track and modify the audio signals in terms of volume, timbre, tempo, and other musical elements. There have been research efforts to imitate the DJ mixing techniques but they are mainly rule-based approaches based on domain knowledge. In this paper, we propose a method to analyze the DJ mixer control from real-world DJ mixes toward a data-driven approach to imitate the DJ performance. Specifically, we estimate the mixing gain trajectories between the two tracks using sub-band analysis with constrained convex optimization. We evaluate the method by reconstructing the original tracks using the two source tracks and the gain estimate, and show that the proposed method outperforms the linear crossfading as a baseline and the single-band analysis. A listening test from the survey of 14 participants also confirms that our proposed method is superior among them.