Detection of clicks in audio signals using warped linear prediction

Esquef, Paulo A. A.; Karjalainen, Matti; Välimäki, Vesa

doi:10.1109/icdsp.2002.1028279

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…For audio signals, several time-domain algorithms have been developed to detect and remove impulse noise. [1][2][3] However, these algorithms do not exploit the differences in spectral and temporal characteristics of speech and impulse noise to maximize the detection performance.…”

Section: Introductionmentioning

confidence: 99%

Impulse-noise suppression in speech using the stationary wavelet transform

Nongpiur

Shpak

2013

The Journal of the Acoustical Society of America

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An approach for detecting and removing impulse noise from speech using the wavelet transform is proposed. The approach utilizes the multi-resolution property of the wavelet transform, which provides finer time resolution at higher frequencies than the short-time Fourier transform to effectively identify and remove impulse noise. The paper then describes how the impulse-detection performance is dependent on certain wavelet features and their relationships with the impulse noise and the underlying speech signal. Performance comparisons carried out with an existing method show that the wavelet approach yields much better features for detecting the impulses. To remove the impulses, an algorithm that uses the stationary wavelet transform has been developed. The algorithm uses a two-step approach where the wavelet coefficients corresponding to the impulses are suppressed in the first step and then substituted by suitable coefficients located within the vicinity of the impulse in the second step. Performance evaluations with an existing method show that the proposed algorithm gives superior results.

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Section: Introductionmentioning

confidence: 99%

Impulse-noise suppression in speech using the stationary wavelet transform

Nongpiur

Shpak

2013

The Journal of the Acoustical Society of America

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“…Adler et al first used the term "audio inpainting" to describe the restoration of gaps in audio signals [1], adopting the name from the image inpainting literature. However this is an old problem in audio processing, and the same task has been previously referred to in the literature as audio interpolation [4,20,21], audio extrapolation [22,23], reconstruction of missing samples [24,25], waveform substitution [5], and imputation [26], among other things. The first methods used interpolation techniques based on the observed samples surrounding the gap [4].…”

Section: Existing Audio Inpainting Methodsmentioning

confidence: 99%

“…The first methods used interpolation techniques based on the observed samples surrounding the gap [4]. A family of successful methods uses autoregressive modeling based on the assumption that the signal is stationary and can be approximated by a linear combination of past samples [4,20,21].…”

Section: Existing Audio Inpainting Methodsmentioning

confidence: 99%

Diffusion-Based Audio Inpainting

Moliner,

Välimäki

2024

J. Audio Eng. Soc.

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Audio inpainting aims to reconstruct missing segments in corrupted recordings. Most existing methods produce plausible reconstructions when the gap lengths are short but struggle to reconstruct gaps larger than about 100 ms. This paper explores diffusion models, a recent class of deep learning models, for the task of audio inpainting. The proposed method uses an unconditionally trained generative model, which can be conditioned in a zero-shot fashion for audio inpainting and is able to regenerate gaps of any size. An improved deep neural network architecture based on the constant-Q transform that allows the model to exploit pitch-equivariant symmetries in audio is also presented. The performance of the proposed algorithm is evaluated through objective and subjective metrics for the task of reconstructing short to mid-sized gaps, up to 300 ms. The results of a formal listening test indicate that, for short gaps in the range of 50 ms, the proposed method delivers performance comparable to the baselines. For wider gaps up to 300 ms long, the authors' method outperforms the baselines and retains good or fair audio quality. The method presented in this paper can be applied to restoring sound recordings that suffer from severe local disturbances or dropouts.

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“…However, it was found that the algorithm performed well for three datasets, but very poorly for two others. Another proposition utilizes warped linear prediction on the frequency domain of audio data by using bilinear conformal mapping to emphasize outliers in higher frequencies [30].…”

Section: E Mean Absolute Spectral Deviationmentioning

confidence: 99%

Predictive Noise Detection in Gramophone Records

Stallmann

Engelbrecht

2016

IJSPS

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Gramophones have regained widespread popularity over the past few years. Being an analogue storage medium, gramophone records are subject to distortions which are mainly caused by scratches. This paper empirically analyses various outlier detection algorithms and proposes a novel predictive approach for noise detection. Twelve different forecasting models were utilized for the predictive deviation method. Once outliers are identified, they can be reconstructed using interpolation algorithms or time series approximation. Experiments were conducted on 800 songs from eight genres, both with artificial and real gramophone noise. The algorithms were compared according to their detection rate, computational speed and the tradeoff between accuracy and speed. It was found that the novel absolute predictive deviation using the autoregressive integrate moving average model performed best overall. The experiments also indicated that it was easier to detect noise in stable signals from genres, compared to noise in volatile signals.

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Detection of clicks in audio signals using warped linear prediction

Cited by 17 publications

References 7 publications

Impulse-noise suppression in speech using the stationary wavelet transform

Impulse-noise suppression in speech using the stationary wavelet transform

Diffusion-Based Audio Inpainting

Predictive Noise Detection in Gramophone Records

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