5 accepted papers
We introduce a data-driven approach to automatic pitch correction of solo singing performances. The proposed approach predicts note-wise pitch shifts from the relationship between the respective spectrograms of the singing and accompaniment. This approach differs from commercial systems, where vocal…
We propose to pre-stack a U-Net as a way of improving the polyphonic music transcription performance of various baseline Convolutional Neural Networks (CNNS). The U-Net, a network architecture based on skip-connections between layers acts as a transformation network followed by a transcription netwo…
We introduce the "Intonation" dataset of amateur vocal performances with a tendency for good intonation, collected from Smule, Inc. The dataset can be used for music information retrieval tasks such as autotuning, query by humming, and singing style analysis. It is available upon request on the Stan…
There are many applications scenarios for which the computational performance and memory footprint of the prediction phase of Deep Neural Networks (DNNs) need to be optimized. Binary Deep Neural Networks (BDNNs) have been shown to be an effective way of achieving this objective. In this pape…
Investigating singing style is a difficult problem as individual styles are intertwined with melodies from different songs. In this paper, a methodology to investigate singing style is proposed. The proposed approach utilizes convolutional neural networks in a siamese architecture. In addition, we i…