The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning targets in a multi-task learning scenario. These approaches, however, require additional annotations such as musical scores, instrument labels, etc. in training and possibly during inference. The available datasets for source separation do not usually provide these additional annotations. In this work, we explore transfer learning strategies to incorporate VGGish features with a state-of-the-art source separation model; VGGish features are known to be a very condensed representation of audio content and have been successfully used in many MIR tasks. We introduce t...
Making machines that can understand musical structure has long been one of the holy grails of audio ...
International audienceBlind source separation usually obtains limited performance on real and polyph...
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for ...
Feature representations derived from models pre-trained on large-scale datasets have shown their gen...
Musical source separation is a complex topic that has been extensively explored in the signal proces...
Comunicació presentada a: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and ...
Abstract Humans are able to distinguish between various sound sources in their environment and se...
Recent advances of music source separation have achieved high quality of vocal isolation from mix au...
This report summarizes the research, methodologies, and experimental implementation on Music Source ...
Currently, most successful source separation techniques use magnitude spectrograms as input, and are...
Despite phenomenal progress in recent years, state-of-the-art music separation systems produce sourc...
This paper discusses the concept of transfer learning and its potential applications to MIR tasks su...
In recent years, music source separation has been one of the most intensively studied research areas...
Very few large-scale music research datasets are publicly available. There is an increasing need for...
Comunicació presentada al INTERSPEECH 2019: The Annual Conference of the International Speech Commun...
Making machines that can understand musical structure has long been one of the holy grails of audio ...
International audienceBlind source separation usually obtains limited performance on real and polyph...
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for ...
Feature representations derived from models pre-trained on large-scale datasets have shown their gen...
Musical source separation is a complex topic that has been extensively explored in the signal proces...
Comunicació presentada a: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and ...
Abstract Humans are able to distinguish between various sound sources in their environment and se...
Recent advances of music source separation have achieved high quality of vocal isolation from mix au...
This report summarizes the research, methodologies, and experimental implementation on Music Source ...
Currently, most successful source separation techniques use magnitude spectrograms as input, and are...
Despite phenomenal progress in recent years, state-of-the-art music separation systems produce sourc...
This paper discusses the concept of transfer learning and its potential applications to MIR tasks su...
In recent years, music source separation has been one of the most intensively studied research areas...
Very few large-scale music research datasets are publicly available. There is an increasing need for...
Comunicació presentada al INTERSPEECH 2019: The Annual Conference of the International Speech Commun...
Making machines that can understand musical structure has long been one of the holy grails of audio ...
International audienceBlind source separation usually obtains limited performance on real and polyph...
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for ...