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MA. A.. Martinez Ramirez, and JO. D.. Reiss, "Analysis and Prediction of the Audio Feature Space when Mixing Raw Recordings into Individual Stems," Paper 9848, (2017 October.). doi: MA. A.. Martinez Ramirez, and JO. D.. Reiss, "Analysis and Prediction of the Audio Feature Space when Mixing Raw Recordings into Individual Stems," Paper 9848, (2017 October.). doi:
Abstract: Processing individual stems from raw recordings is one of the first steps of multitrack audio mixing. In this work we explore which set of low-level audio features are sufficient to design a prediction model for this transformation. We extract a large set of audio features from bass, guitar, vocal, and keys raw recordings and stems. We show that a procedure based on random forests classifiers can lead us to reduce significantly the number of features and we use the selected audio features to train various multi-output regression models. Thus, we investigate stem processing as a content-based transformation, where the inherent content of raw recordings leads us to predict the change of feature values that occurred within the transformation.

@article{martinez ramirez2017analysis,
author={martinez ramirez, marco a. and reiss, joshua d.},
journal={journal of the audio engineering society},
title={analysis and prediction of the audio feature space when mixing raw recordings into individual stems},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},} @article{martinez ramirez2017analysis,
author={martinez ramirez, marco a. and reiss, joshua d.},
journal={journal of the audio engineering society},
title={analysis and prediction of the audio feature space when mixing raw recordings into individual stems},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={processing individual stems from raw recordings is one of the first steps of multitrack audio mixing. in this work we explore which set of low-level audio features are sufficient to design a prediction model for this transformation. we extract a large set of audio features from bass, guitar, vocal, and keys raw recordings and stems. we show that a procedure based on random forests classifiers can lead us to reduce significantly the number of features and we use the selected audio features to train various multi-output regression models. thus, we investigate stem processing as a content-based transformation, where the inherent content of raw recordings leads us to predict the change of feature values that occurred within the transformation.},}

TY - paper
TI - Analysis and Prediction of the Audio Feature Space when Mixing Raw Recordings into Individual Stems
SP - EP -
AU - Martinez Ramirez, Marco A.
AU - Reiss, Joshua D.
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017 TY - paper
TI - Analysis and Prediction of the Audio Feature Space when Mixing Raw Recordings into Individual Stems
SP - EP -
AU - Martinez Ramirez, Marco A.
AU - Reiss, Joshua D.
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017
AB - Processing individual stems from raw recordings is one of the first steps of multitrack audio mixing. In this work we explore which set of low-level audio features are sufficient to design a prediction model for this transformation. We extract a large set of audio features from bass, guitar, vocal, and keys raw recordings and stems. We show that a procedure based on random forests classifiers can lead us to reduce significantly the number of features and we use the selected audio features to train various multi-output regression models. Thus, we investigate stem processing as a content-based transformation, where the inherent content of raw recordings leads us to predict the change of feature values that occurred within the transformation.