13 citations · 23 across the 4 of their papers we have counts for
6 papers
Metric Learning vs Classification for Disentangled Music Representation Learning
Jongpil Lee, Nicholas J. Bryan, Justin Salamon +2
Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two centr…
Disentangled Multidimensional Metric Learning for Music Similarity
Jongpil Lee, Nicholas J. Bryan, Justin Salamon +2
Music similarity search is useful for a variety of creative tasks such as replacing one music recording with another recording with a similar "feel", a common task in video editing…
Controllable Neural Prosody Synthesis
Max Morrison, Zeyu Jin, Justin Salamon +2
Speech synthesis has recently seen significant improvements in fidelity, driven by the advent of neural vocoders and neural prosody generators. However, these systems lack intuitiv…
Few-Shot Drum Transcription in Polyphonic Music
Yu Wang, Justin Salamon, Mark Cartwright +2
Data-driven approaches to automatic drum transcription (ADT) are often limited to a predefined, small vocabulary of percussion instrument classes. Such models cannot recognize out-…
A Differentiable Perceptual Audio Metric Learned from Just Noticeable Differences
Pranay Manocha, Adam Finkelstein, Richard Zhang +3
Many audio processing tasks require perceptual assessment. The ``gold standard`` of obtaining human judgments is time-consuming, expensive, and cannot be used as an optimization cr…
Impulse Response Data Augmentation and Deep Neural Networks for Blind Room Acoustic Parameter Estimation
Nicholas J. Bryan
The reverberation time (T60) and the direct-to-reverberant ratio (DRR) are commonly used to characterize room acoustic environments. Both parameters can be measured from an acousti…