  Singing voice separation and vocal pitch estimation are pivotal tasks in
music information retrieval. Existing methods for simultaneous extraction of
clean vocals and vocal pitches can be classified into two categories: pipeline
methods and naive joint learning methods. However, the efficacy of these
methods is limited by the following problems: On the one hand, pipeline methods
train models for each task independently, resulting a mismatch between the data
distributions at the training and testing time. On the other hand, naive joint
learning methods simply add the losses of both tasks, possibly leading to a
misalignment between the distinct objectives of each task. To solve these
problems, we propose a Deep Joint Cascade Model (DJCM) for singing voice
separation and vocal pitch estimation. DJCM employs a novel joint cascade model
structure to concurrently train both tasks. Moreover, task-specific weights are
used to align different objectives of both tasks. Experimental results show
that DJCM achieves state-of-the-art performance on both tasks, with great
improvements of 0.45 in terms of Signal-to-Distortion Ratio (SDR) for singing
voice separation and 2.86% in terms of Overall Accuracy (OA) for vocal pitch
estimation. Furthermore, extensive ablation studies validate the effectiveness
of each design of our proposed model. The code of DJCM is available at
https://github.com/Dream-High/DJCM .
