  A Query-By-Humming (QBH) system constitutes a particular case of music
information retrieval where the input is a user-hummed melody and the output is
the original song which contains that melody. A typical QBH system consists of
melody extraction and candidate melody retrieval.
  For melody extraction, accurate note transcription is the key enabling
technology. However, current transcription methods are unable to definitively
capture the melody and address inaccuracies in user-hummed queries. In this
paper, we incorporate Total Variation Regularization (TVR) to denoise queries.
This approach accounts for user error in humming without loss of meaningful
data and reliably captures the underlying melody.
  For candidate melody retrieval, we employ a deep learning approach to time
series classification using a Fully Convolutional Neural Network. The trained
network classifies the incoming query as belonging to one of the target songs.
  For our experiments, we use Roger Jang's MIR-QBSH dataset which is the
standard MIREX dataset. We demonstrate that inclusion of TVR denoised queries
in the training set enhances the overall accuracy of the system to 93% which is
higher than other state-of-the-art QBH systems.
