3 citations · 4 across the 2 of their papers we have counts for
5 papers
Rethinking Evaluation Methodology for Audio-to-Score Alignment
John Thickstun, Jennifer Brennan, Harsh Verma
This paper offers a precise, formal definition of an audio-to-score alignment. While the concept of an alignment is intuitively grasped, this precision affords us new insight into…
Source Separation with Deep Generative Priors
Vivek Jayaram, John Thickstun
Despite substantial progress in signal source separation, results for richly structured data continue to contain perceptible artifacts. In contrast, recent deep generative models c…
Convolutional Composer Classification
Harsh Verma, John Thickstun
This paper investigates end-to-end learnable models for attributing composers to musical scores. We introduce several pooled, convolutional architectures for this task and draw con…
Coupled Recurrent Models for Polyphonic Music Composition
John Thickstun, Zaid Harchaoui, Dean P. Foster +1
This paper introduces a novel recurrent model for music composition that is tailored to the structure of polyphonic music. We propose an efficient new conditional probabilistic fac…
Invariances and Data Augmentation for Supervised Music Transcription
John Thickstun, Zaid Harchaoui, Dean Foster +1
This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-i…