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A Query-By-Humming (QBH) system constitutes a particular case of musicinformation retrieval where the input is a user-hummed melody and the output isthe original song which contains that melody. A typical QBH system consists ofmelody extraction and candidate melody retrieval.  For melody extraction, accurate note transcription is the key enablingtechnology. However, current transcription methods are unable to definitivelycapture the melody and address inaccuracies in user-hummed queries. In thispaper, we incorporate Total Variation Regularization (TVR) to denoise queries.This approach accounts for user error in humming without loss of meaningfuldata and reliably captures the underlying melody.  For candidate melody retrieval, we employ a deep learning approach to timeseries classification using a Fully Convolutional Neural Network. The trainednetwork 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 thestandard MIREX dataset. We demonstrate that inclusion of TVR denoised queriesin the training set enhances the overall accuracy of the system to 93% which ishigher than other state-of-the-art QBH systems.

Incorporating Total Variation Regularization in the design of an intelligent Query by Humming system