STFT Phase Reconstruction in Voiced Speech for an Improved Single-Channel Speech Enhancement

Krawczyk, Martin; Gerkmann, Timo

doi:10.1109/taslp.2014.2354236

Cited by 186 publications

(163 citation statements)

References 27 publications

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“…A summary of each of the 16 primary systems is provided in Table III and discussed below. [42], baseband phase difference (BPD) [43], and pitch synchronous phase (PSP). A multilayer perceptron (MLP) was trained for each feature.…”

Section: B Submissionsmentioning

confidence: 99%

ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

Yamagishi

Kinnunen

et al. 2017

IEEE J. Sel. Top. Signal Process.

256

302

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Abstract-Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing countermeasures. This paper describes the ASV Spoofing and Countermeasures (ASVspoof) initiative, which aims to fill this void. Through the provision of a common dataset, protocols, and metrics, ASVspoof promotes a sound research methodology and fosters technological progress. This paper also describes the ASVspoof 2015 dataset, evaluation, and results with detailed analyses. A review of post-evaluation studies conducted using the same dataset illustrates the rapid progress stemming from ASVspoof and outlines the need for further investigation. Priority future research directions are presented in the scope of the next ASVspoof evaluation planned for 2017.

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Section: B Submissionsmentioning

confidence: 99%

ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

Yamagishi

Kinnunen

et al. 2017

IEEE J. Sel. Top. Signal Process.

256

302

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“…• BPD: Baseband phase difference [20] is a phase feature extracted from baseband STFT, which can also provide a clear pattern to present phase information.…”

Section: Feature Extractionmentioning

confidence: 99%

Spoofing detection from a feature representation perspective

Tian

Xiao³

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Spoofing detection, which discriminates the spoofed speech from the natural speech, has gained much attention recently. Lowdimensional features that are used in speaker recognition/verification are also used in spoofing detection. Unfortunately, they don't capture sufficient information required for spoofing detection. In this work, we investigate the use of high-dimensional features for spoofing detection, that maybe more sensitive to the artifacts in the spoofed speech. Six types of high-dimensional feature are employed. For each kind of feature, four different representations are extracted, i.e. the original high-dimensional feature, corresponding low-dimensional feature, the low-and the high-frequency regions of the original high-dimensional feature. Dynamic features are also calculated to assess the effectiveness of the temporal information to detect the artifacts across frames. A neural network-based classifier is adopted to handle the high-dimensional features. Experimental results on the standard ASVspoof 2015 corpus suggest that highdimensional features and dynamic features are useful for spoofing attack detection. A fusion of them has been shown to achieve 0.0% the equal error rates for nine of ten attack types.

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“…Alternatively, one can extract phase constraints from the sinusoidal model, which is widely used for representing audio signals [11,22]. It can be shown [23] that the STFT phase µ of a signal modeled as a sum of sinusoids in the time domain follows the phase unwrapping (PU) equation:…”

Section: Sinusoidal Modelmentioning

confidence: 99%

“…It has been used in many audio applications, including time stretching [23], speech enhancement [22] and source separation [7,11,24].…”

Section: Sinusoidal Modelmentioning

confidence: 99%

Reducing Interference with Phase Recovery in DNN-based Monaural Singing Voice Separation

et al. 2018

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State-of-the-art methods for monaural singing voice separation consist in estimating the magnitude spectrum of the voice in the short-time Fourier transform (STFT) domain by means of deep neural networks (DNNs). The resulting magnitude estimate is then combined with the mixture's phase to retrieve the complex-valued STFT of the voice, which is further synthesized into a time-domain signal. However, when the sources overlap in time and frequency, the STFT phase of the voice differs from the mixture's phase, which results in interference and artifacts in the estimated signals. In this paper, we investigate on recent phase recovery algorithms that tackle this issue and can further enhance the separation quality. These algorithms exploit phase constraints that originate from a sinusoidal model or from consistency, a property that is a direct consequence of the STFT redundancy. Experiments conducted on real music songs show that those algorithms are efficient for reducing interference in the estimated voice compared to the baseline approach.

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STFT Phase Reconstruction in Voiced Speech for an Improved Single-Channel Speech Enhancement

Cited by 186 publications

References 27 publications

ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

Spoofing detection from a feature representation perspective

Reducing Interference with Phase Recovery in DNN-based Monaural Singing Voice Separation

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