Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
83
Fast signal reconstruction from magnitude stft spectrogram based on spectrogram consistency
- Proc. of International Conference on Digital Audio Effects DAFx ’10
, 2010
"... The modification of magnitude spectrograms is at the core of many audio signal processing methods, from source separation to sound modification or noise canceling, and reconstructing a natu-ral sounding signal in such situations is thus a very important issue. This article presents recent theoretica ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
The modification of magnitude spectrograms is at the core of many audio signal processing methods, from source separation to sound modification or noise canceling, and reconstructing a natu-ral sounding signal in such situations is thus a very important issue. This article presents recent
Max-gabor analysis and synthesis of spectrograms
- in Proc. ICSLP
, 2006
"... We present a method that analyzes a two-dimensional magnitude spectrogram S(f, t) into its local constituent spectro-temporal amplitudes A(f,t), frequencies F(f, t), orientations Θ(f, t), and phases φ(f, t). The method operates by performing a twodimensional local Gabor-like analysis of the spectrog ..."
Abstract
-
Cited by 5 (4 self)
- Add to MetaCart
We present a method that analyzes a two-dimensional magnitude spectrogram S(f, t) into its local constituent spectro-temporal amplitudes A(f,t), frequencies F(f, t), orientations Θ(f, t), and phases φ(f, t). The method operates by performing a twodimensional local Gabor-like analysis
Explicit consistency constraints for STFT spectrograms and their application to phase reconstruction
- in Proc
, 2008
"... As many acoustic signal processing methods, for example for source separation or noise canceling, operate in the magnitude spectrogram domain, the problem of reconstructing a perceptually good sounding signal from a modified magnitude spectrogram, and more generally to understand what makes a spectr ..."
Abstract
-
Cited by 11 (5 self)
- Add to MetaCart
As many acoustic signal processing methods, for example for source separation or noise canceling, operate in the magnitude spectrogram domain, the problem of reconstructing a perceptually good sounding signal from a modified magnitude spectrogram, and more generally to understand what makes a
Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria
- IEEE Trans. On Audio, Speech and Lang. Processing
, 2007
"... Abstract—An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time-varying gain ..."
Abstract
-
Cited by 189 (30 self)
- Add to MetaCart
Abstract—An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time
AM-FM DEMODULATION OF SPECTROGRAMS USING LOCALIZED 2D MAX-GABOR ANALYSIS
"... We present a method that de-modulates a narrowband magnitude spectrogram S(f, t) into a frequency modulation term cos(φ(f, t)) which represents the underlying harmonic carrier, and an amplitude modulation term A(f, t) which represents the spectral envelope. Our method operates by performing a two-di ..."
Abstract
-
Cited by 7 (4 self)
- Add to MetaCart
We present a method that de-modulates a narrowband magnitude spectrogram S(f, t) into a frequency modulation term cos(φ(f, t)) which represents the underlying harmonic carrier, and an amplitude modulation term A(f, t) which represents the spectral envelope. Our method operates by performing a two
MORPHOLOGICAL FILTERING OF SPECTROGRAMS FOR AUTOMATIC SPEECH RECOGNITION
"... This paper examines the separation of speech signals from additive noise using a recently proposed signal, noise segmentation approach based on statistical properties of the spectrogram [1,2]. Competitive ASR results were reported in [3] despite using only crude spectrogram shape information suggest ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
suggesting that the approach offers high reliability in identifying regions of different signal dominance and might be robust down to negative SNRs. This paper extends these early results in two directions. First extension investigates the contribution of spectrogram shapes plus magnitudes versus shapes
Instantaneous Frequency Estimation Based On The Robust Spectrogram
, 2001
"... Robust M-periodogram is defined for the analysis of signals with heavy-tailed distribution noise. In the form of a robust spectrogram (RSPEC) it can be used for the analysis of nonstationary signals. In this paper a RSPEC based instantaneous frequency (IF) estimator, with a timevarying window length ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
Robust M-periodogram is defined for the analysis of signals with heavy-tailed distribution noise. In the form of a robust spectrogram (RSPEC) it can be used for the analysis of nonstationary signals. In this paper a RSPEC based instantaneous frequency (IF) estimator, with a timevarying window
Combining Pitch-Based Inference and Non-Negative Spectrogram Factorization in Separating Vocals from Polyphonic Music
"... This paper proposes a novel algorithm for separating vocals from polyphonic music accompaniment. Based on pitch estimation, the method first creates a binary mask indicating timefrequency segments in the magnitude spectrogram where harmonic content of the vocal signal is present. Second, nonnegative ..."
Abstract
-
Cited by 20 (3 self)
- Add to MetaCart
This paper proposes a novel algorithm for separating vocals from polyphonic music accompaniment. Based on pitch estimation, the method first creates a binary mask indicating timefrequency segments in the magnitude spectrogram where harmonic content of the vocal signal is present. Second
Latent variable decomposition of spectrograms for single channel speaker separation
- in IEEE WASPAA
, 2005
"... In this paper we present an algorithm for the separation of multiple speakers from mixed singlechannel recordings by latent variable decompoistion of the speech spectrogram. We model each magnitude spectral vector in the short-time Fourier transform of a speech signal as the outcome of a discrete ra ..."
Abstract
-
Cited by 23 (12 self)
- Add to MetaCart
In this paper we present an algorithm for the separation of multiple speakers from mixed singlechannel recordings by latent variable decompoistion of the speech spectrogram. We model each magnitude spectral vector in the short-time Fourier transform of a speech signal as the outcome of a discrete
Results 1 - 10
of
83
