Sorry, you need to enable JavaScript to visit this website.

Content-Based Audio Processing

AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS


In this paper we present the INESC Key Detection (IKD) system which incorporates a novel method for dynamically biasing key mode estimation using the spatial displacement of beat-synchronous Tonal Interval Vectors (TIVs). We evaluate the performance of the IKD system at finding the global key on three annotated audio datasets and using three key-defining profiles.

Paper Details

Authors:
Gilberto Bernardes, Matthew E. P. Davies, and Carlos Guedes
Submitted On:
1 March 2017 - 2:13pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster_ICASSP.pdf

(482 downloads)

Keywords

Subscribe

[1] Gilberto Bernardes, Matthew E. P. Davies, and Carlos Guedes, "AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1553. Accessed: Aug. 22, 2017.
@article{1553-17,
url = {http://sigport.org/1553},
author = {Gilberto Bernardes; Matthew E. P. Davies; and Carlos Guedes },
publisher = {IEEE SigPort},
title = {AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS},
year = {2017} }
TY - EJOUR
T1 - AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS
AU - Gilberto Bernardes; Matthew E. P. Davies; and Carlos Guedes
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1553
ER -
Gilberto Bernardes, Matthew E. P. Davies, and Carlos Guedes. (2017). AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS. IEEE SigPort. http://sigport.org/1553
Gilberto Bernardes, Matthew E. P. Davies, and Carlos Guedes, 2017. AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS. Available at: http://sigport.org/1553.
Gilberto Bernardes, Matthew E. P. Davies, and Carlos Guedes. (2017). "AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS." Web.
1. Gilberto Bernardes, Matthew E. P. Davies, and Carlos Guedes. AUTOMATIC MUSICAL KEY ESTIMATION WITH ADAPTIVE MODE BIAS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1553

COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES


We approach cover song identification using a novel time-series representation of audio based on the 2DFT. The audio is represented as a sequence of magnitude 2D Fourier Transforms (2DFT). This representation is robust to key changes, timbral changes, and small local tempo deviations. We look at cross-similarity between these time-series, and extract a distance measure that is invariant to music structure changes. Our approach is state-of-the-art on a recent cover song dataset, and expands on previous work using the 2DFT for music representation and work on live song recognition.

Paper Details

Authors:
Prem Seetharaman, Zafar Rafii
Submitted On:
27 February 2017 - 3:00pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

cover_song_poster.pdf

(63 downloads)

Keywords

Subscribe

[1] Prem Seetharaman, Zafar Rafii, "COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1445. Accessed: Aug. 22, 2017.
@article{1445-17,
url = {http://sigport.org/1445},
author = {Prem Seetharaman; Zafar Rafii },
publisher = {IEEE SigPort},
title = {COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES},
year = {2017} }
TY - EJOUR
T1 - COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES
AU - Prem Seetharaman; Zafar Rafii
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1445
ER -
Prem Seetharaman, Zafar Rafii. (2017). COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES. IEEE SigPort. http://sigport.org/1445
Prem Seetharaman, Zafar Rafii, 2017. COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES. Available at: http://sigport.org/1445.
Prem Seetharaman, Zafar Rafii. (2017). "COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES." Web.
1. Prem Seetharaman, Zafar Rafii. COVER SONG IDENTIFICATION WITH 2D FOURIER TRANSFORM SEQUENCES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1445

Conjugate gradient acceleration of non-linear smoothing filters


Noisy signal

The most efficient signal edge-preserving smoothing filters, e.g., for denoising, are non-linear. Thus, their acceleration is challenging and is often done in practice by tuning filters parameters, such as increasing the width of the local smoothing neighborhood, resulting in more aggressive smoothing of a single sweep at the cost of increased edge blurring. We propose an alternative technology, accelerating the original filters without tuning, by running them through a conjugate gradient method, not affecting their quality.

GlobalSIP.pdf

PDF icon GlobalSIP.pdf (256 downloads)

Paper Details

Authors:
Alexander Malyshev
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP.pdf

(256 downloads)

Keywords

Subscribe

[1] Alexander Malyshev, "Conjugate gradient acceleration of non-linear smoothing filters", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/406. Accessed: Aug. 22, 2017.
@article{406-15,
url = {http://sigport.org/406},
author = {Alexander Malyshev },
publisher = {IEEE SigPort},
title = {Conjugate gradient acceleration of non-linear smoothing filters},
year = {2015} }
TY - EJOUR
T1 - Conjugate gradient acceleration of non-linear smoothing filters
AU - Alexander Malyshev
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/406
ER -
Alexander Malyshev. (2015). Conjugate gradient acceleration of non-linear smoothing filters. IEEE SigPort. http://sigport.org/406
Alexander Malyshev, 2015. Conjugate gradient acceleration of non-linear smoothing filters. Available at: http://sigport.org/406.
Alexander Malyshev. (2015). "Conjugate gradient acceleration of non-linear smoothing filters." Web.
1. Alexander Malyshev. Conjugate gradient acceleration of non-linear smoothing filters [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/406