Declipping of Audio Signals Using Perceptual Compressed Sensing

Defraene, Bruno; Mansour, Naim; Hertogh, Steven De; Waterschoot, Toon van; Diehl, Moritz; Moonen, Marc

doi:10.1109/tasl.2013.2281570

Cited by 53 publications

(50 citation statements)

References 25 publications

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“…Spectral masking is usually modeled with the so-called “spreading function” of masking which, in its simplest form, is an asymmetric triangular function that approximates the general shape of masking patterns for narrowband maskers (e.g. [6, 8, 11, 22]). Temporal (forward) masking is usually modeled using a linear function of log(Δ T ) (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, sparsity-based approaches combine TF decompositions and masking models to reduce the amount of nonzero TF coefficients [8, 9]. Audio restoration techniques (e.g., [10, 11]) also combine TF transforms and masking models to retrieve only the perceptually relevant information. As a last example, source separation algorithms estimate binary masks to weight the TF coefficients of sound mixtures based on auditory masking in order to separate the signal(s) of interest [12, 13].…”

Section: Introductionmentioning

confidence: 99%

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Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli

et al. 2016

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Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli

et al. 2016

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“…Other areas of audio processing also benefit from ANNs, such as audio classification [13], feature extraction [14], and audio declipping [15]. However, comparatively little research has been done on the detection and removal of impulse disturbances from musical signals.…”

Section: State Of the Artmentioning

confidence: 99%

Gramophone Noise Detection and Reconstruction Using Time Delay Artificial Neural Networks

Stallmann

Engelbrecht

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Gramophone records were the main recording medium for more than seven decades and regained widespread popularity over the past several years. Being an analogue storage medium, gramophone records are subject to distortions caused by scratches, dust particles, degradation and other means of improper handling. The observed noise often leads to an unpleasant listening experience and requires a filtering process to remove the unwanted disruptions and improve the audio quality. This article proposes a novel approach that employs various feed forward time delay artificial neural networks to detect and reconstruct noise in musical sound waves. A set of 800 songs from eight different genres were used to validate the performance of the neural networks. The performance was analysed according to the outlier detection and interpolation accuracy, the computational time and the tradeoff between the accuracy and time. The empirical results of both the detection and reconstruction neural networks were compared to a number of other algorithms, including various statistical measurements, duplication approaches, trigonometric processes, polynomials and time series models. It was found that the neural networks' outlier detection accuracy was slightly lower than some of the other noise identification algorithms, but achieved a more efficient tradeoff by detecting most of the noise in real time. The reconstruction process favoured neural networks with an increase in the interpolation accuracy compared to other widely used time series models. It was also found that certain genres such as classical, country and jazz music were interpolated more accurately. Volatile signals, such as electronic, metal and pop music were more challenging to reconstruct and were substantially better interpolated using neural networks than the other examined algorithms.

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“…The psychoacoustic model produces instantaneous global masking threshold of a given audio signal using part of the ISOIIEC 11172-3 MPEG-I Layer I psychoacoustic model [10] which is used in [14]. After obtaining the global masking, it used in forming the perceptual weighting matrix.…”

Section: Proposed Models and Simulations Setupmentioning

confidence: 99%

“…The signals are from the EBU SQAM discs commonly used for assessment of audio coder [13]. Part of these signal are used [14]. The resulting samples are divides into frames and each frames contain 1321 samples and the duration of each frame is 30ms.…”

Section: The Perceptual Sfftmentioning

confidence: 99%

Perceptual Compressed Sensing and perceptual Sparse Fast Fourier Transform for audio signal compression

Kasem

Elsabrouty

2014

2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Sparse signal processing has become central in many communications and multimedia processing applications. Techniques such as Compressed Sensing(CS) and Sparse Fast Fourier Transform (SFFT) provide good quality of the restored signal even when the signal is not completely sparse even also at high compression ratio. In this work, we examine the effect of taking the perceptual properties of the audio signal into account while performing both CS and SFFT. The improved perceptual signal quality by Mean Opinion Score (MOS) is verified.

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Declipping of Audio Signals Using Perceptual Compressed Sensing

Cited by 53 publications

References 25 publications

Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli

Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli

Gramophone Noise Detection and Reconstruction Using Time Delay Artificial Neural Networks

Perceptual Compressed Sensing and perceptual Sparse Fast Fourier Transform for audio signal compression

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