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P. Hoffmann, and B. Kostek, "Bass Enhancement Settings in Portable Devices Based on Music Genre Recognition," J. Audio Eng. Soc., vol. 63, no. 12, pp. 980-989, (2015 December.). doi: https://doi.org/10.17743/jaes.2015.0087 P. Hoffmann, and B. Kostek, "Bass Enhancement Settings in Portable Devices Based on Music Genre Recognition," J. Audio Eng. Soc., vol. 63 Issue 12 pp. 980-989, (2015 December.). doi: https://doi.org/10.17743/jaes.2015.0087
Abstract: The paper presents a novel approach to the Virtual Bass Synthesis (VBS) applied to mobile devices, called Smart VBS (SVBS). The proposed algorithm uses an intelligent, rule-based setting of bass synthesis parameters adjusted to the particular music genre. Harmonic generation is based on a nonlinear device (NLD) method with the intelligent controlling system adapting to the recognized music genre. To automatically classify music genres, the k-Nearest Neighbor classifier combined with the Principal Component Analysis (PCA) method is employed. To fine tune the SVBS algorithm, the MUSHRA test is performed. Subjects are presented with music excerpts belonging to various genres, unprocessed and also processed by SVBS and a conventional bass boost algorithm. Listening tests show that subjects in most cases prefer the SVBS strategy developed by the authors in favor of both the conventional bass boost algorithm and the unprocessed audio file. Furthermore, the listeners indicated that perception of the SVBS-processed music excerpts is similar for several types of portable devices.

@article{hoffmann2016bass,
author={hoffmann, piotr and kostek, bozena},
journal={journal of the audio engineering society},
title={bass enhancement settings in portable devices based on music genre recognition},
year={2016},
volume={63},
number={12},
pages={980-989},
doi={https://doi.org/10.17743/jaes.2015.0087},
month={december},} @article{hoffmann2016bass,
author={hoffmann, piotr and kostek, bozena},
journal={journal of the audio engineering society},
title={bass enhancement settings in portable devices based on music genre recognition},
year={2016},
volume={63},
number={12},
pages={980-989},
doi={https://doi.org/10.17743/jaes.2015.0087},
month={december},
abstract={the paper presents a novel approach to the virtual bass synthesis (vbs) applied to mobile devices, called smart vbs (svbs). the proposed algorithm uses an intelligent, rule-based setting of bass synthesis parameters adjusted to the particular music genre. harmonic generation is based on a nonlinear device (nld) method with the intelligent controlling system adapting to the recognized music genre. to automatically classify music genres, the k-nearest neighbor classifier combined with the principal component analysis (pca) method is employed. to fine tune the svbs algorithm, the mushra test is performed. subjects are presented with music excerpts belonging to various genres, unprocessed and also processed by svbs and a conventional bass boost algorithm. listening tests show that subjects in most cases prefer the svbs strategy developed by the authors in favor of both the conventional bass boost algorithm and the unprocessed audio file. furthermore, the listeners indicated that perception of the svbs-processed music excerpts is similar for several types of portable devices.},}

TY - paper
TI - Bass Enhancement Settings in Portable Devices Based on Music Genre Recognition
SP - 980 EP - 989
AU - Hoffmann, Piotr
AU - Kostek, Bozena
PY - 2016
JO - Journal of the Audio Engineering Society
IS - 12
VO - 63
VL - 63
Y1 - December 2015 TY - paper
TI - Bass Enhancement Settings in Portable Devices Based on Music Genre Recognition
SP - 980 EP - 989
AU - Hoffmann, Piotr
AU - Kostek, Bozena
PY - 2016
JO - Journal of the Audio Engineering Society
IS - 12
VO - 63
VL - 63
Y1 - December 2015
AB - The paper presents a novel approach to the Virtual Bass Synthesis (VBS) applied to mobile devices, called Smart VBS (SVBS). The proposed algorithm uses an intelligent, rule-based setting of bass synthesis parameters adjusted to the particular music genre. Harmonic generation is based on a nonlinear device (NLD) method with the intelligent controlling system adapting to the recognized music genre. To automatically classify music genres, the k-Nearest Neighbor classifier combined with the Principal Component Analysis (PCA) method is employed. To fine tune the SVBS algorithm, the MUSHRA test is performed. Subjects are presented with music excerpts belonging to various genres, unprocessed and also processed by SVBS and a conventional bass boost algorithm. Listening tests show that subjects in most cases prefer the SVBS strategy developed by the authors in favor of both the conventional bass boost algorithm and the unprocessed audio file. Furthermore, the listeners indicated that perception of the SVBS-processed music excerpts is similar for several types of portable devices.