  Singing Voice Detection (SVD) has been an active area of research in music
information retrieval (MIR). Currently, two deep neural network-based methods,
one based on CNN and the other on RNN, exist in literature that learn optimized
features for the voice detection (VD) task and achieve state-of-the-art
performance on common datasets. Both these models have a huge number of
parameters (1.4M for CNN and 65.7K for RNN) and hence not suitable for
deployment on devices like smartphones or embedded sensors with limited
capacity in terms of memory and computation power. The most popular method to
address this issue is known as knowledge distillation in deep learning
literature (in addition to model compression) where a large pre-trained network
known as the teacher is used to train a smaller student network. Given the wide
applications of SVD in music information retrieval, to the best of our
knowledge, model compression for practical deployment has not yet been
explored. In this paper, efforts have been made to investigate this issue using
both conventional as well as ensemble knowledge distillation techniques.
