12 citations · 17 across the 8 of their papers we have counts for
9 papers
Few-Shot Musical Source Separation
Yu Wang, Daniel Stoller, Rachel M. Bittner +1
Deep learning-based approaches to musical source separation are often limited to the instrument classes that the models are trained on and do not generalize to separate unseen inst…
A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation
Rachel M. Bittner, Juan José Bosch, David Rubinstein +2
Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task's complexity, best results have typically bee…
vocadito: A dataset of solo vocals with , note, and lyric annotations
Rachel M. Bittner, Katherine Pasalo, Juan José Bosch +2
To compliment the existing set of datasets, we present a small dataset entitled vocadito, consisting of 40 short excerpts of monophonic singing, sung in 7 different languages by si…
Soundata: A Python library for reproducible use of audio datasets
Magdalena Fuentes, Justin Salamon, Pablo Zinemanas +6
Soundata is a Python library for loading and working with audio datasets in a standardized way, removing the need for writing custom loaders in every project, and improving reprodu…
Audio-based Musical Version Identification: Elements and Challenges
Furkan Yesiler, Guillaume Doras, Rachel M. Bittner +2
In this article, we aim to provide a review of the key ideas and approaches proposed in 20 years of scientific literature around musical version identification (VI) research and co…
Learned complex masks for multi-instrument source separation
Andreas Jansson, Rachel M. Bittner, Nicola Montecchio +1
Music source separation in the time-frequency domain is commonly achieved by applying a soft or binary mask to the magnitude component of (complex) spectrograms. The phase componen…
Data Cleansing with Contrastive Learning for Vocal Note Event Annotations
Gabriel Meseguer-Brocal, Rachel Bittner, Simon Durand +1
Data cleansing is a well studied strategy for cleaning erroneous labels in datasets, which has not yet been widely adopted in Music Information Retrieval. Previously proposed data…
Neural Music Synthesis for Flexible Timbre Control
Jong Wook Kim, Rachel Bittner, Aparna Kumar +1
The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process --- creating audio samples from a s…
Multitask Learning for Fundamental Frequency Estimation in Music
Rachel M. Bittner, Brian McFee, Juan P. Bello
Fundamental frequency (f0) estimation from polyphonic music includes the tasks of multiple-f0, melody, vocal, and bass line estimation. Historically these problems have been approa…