Technical Reports - Query Results
Your query term was 'number = 2010-08'1 report found
- OFAI-TR-2010-08 (
918kB PDF file)Sparse Regression in Time-Frequency Representations of Complex Audio
- Monika Doerfler, Gino Velasco, Arthur Flexer, Volkmar Klien
- Time-frequency representations are commonly used tools
for the representation of audio and in particular music signals.
From a theoretical point of view, these representations
are linked to Gabor frames. Frame theory yields a
convenient reconstruction method making post-processing
unnecessary. Furthermore, using dual or tight frames in the
reconstruction, we may resynthesize localized components
from so-called sparse representation coefficients. Sparsity
of coefficients is directly reinforced by the application of
a ℓ1-penalization term on the coefficients. We introduce
an iterative algorithm leading to sparse coefficients and
demonstrate the effect of using these coefficients in several
examples. In particular, we are interested in the ability
of a sparsity promoting approach to the task of separating
components with overlapping analysis coefficients in the
time-frequency domain. We also apply our approach to the
problem of auditory scene description, i.e. source identification
in a complex audio mixture.
Keywords: Signal Processing, Audio, Sparsity, Annotation
- Time-frequency representations are commonly used tools
for the representation of audio and in particular music signals.
From a theoretical point of view, these representations
are linked to Gabor frames. Frame theory yields a
convenient reconstruction method making post-processing
unnecessary. Furthermore, using dual or tight frames in the
reconstruction, we may resynthesize localized components
from so-called sparse representation coefficients. Sparsity
of coefficients is directly reinforced by the application of
a ℓ1-penalization term on the coefficients. We introduce
an iterative algorithm leading to sparse coefficients and
demonstrate the effect of using these coefficients in several
examples. In particular, we are interested in the ability
of a sparsity promoting approach to the task of separating
components with overlapping analysis coefficients in the
time-frequency domain. We also apply our approach to the
problem of auditory scene description, i.e. source identification
in a complex audio mixture.