Significance of group delay functions in spectrum estimation

Yegnanarayana, B.; Murthy, Hema A.

doi:10.1109/78.157227

Cited by 111 publications

(62 citation statements)

References 6 publications

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“…• GD: Group delay [21] is a representation of filter phase response, which is defined as the negative derivative of the Fourier transform phase. It is a frame-based feature, used to capture the phase distortion along frequency axis.…”

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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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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“…However, the phase characteristics of speech are also found to be significant as the magnitude for speaker characterization [7], [8]. Studies have been reported to use Fourier transform phase-based features, such as, Modified Group Delay (MGD) features [9] and [10], temporal modulation [11] to detect genuine vs. impostor speech. Another way to decompose a signal, is by using Hilbert transform, it is the product of a slowly-changing envelope and a rapidly-changing fine time structure.…”

Section: Introductionmentioning

confidence: 99%

Novel energy separation based instantaneous frequency features for spoof speech detection

Kamble

Patil

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Speech Synthesis (SS) and Voice Conversion (VC) presents a genuine risk of attacks for Automatic Speaker Verification (ASV) technology. In this paper, we evaluate front-end anti-spoofing technique to protect ASV system for SS and VC attack using a standard benchmarking database. In particular, we propose a novel feature set, namely, Energy Separation Algorithm-based Instantaneous Frequency Cosine Coefficients (ESA-IFCC) to detect the genuine and impostor speech. The experiments are carried out on ASV Spoof 2015 Challenge database. On the development set, the score-level fusion of proposed ESA-IFCC feature set with Mel Frequency Cepstral Coefficients (MFCC) gave an EER of 3.45 %, which reduced significantly from MFCC (6.98 %) and ESA-IFCC (5.43 %) with 13-D static features. The EER decreases further to 2.01 % and 1.89 % for ∆ and ∆∆ features derived from proposed ESA-IFCC features, respectively. The overall average error rate for known and unknown attacks in evaluation set was 6.79 % for ESA-IFCC and was significantly better than the MFCC (9.15 %) features.

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“…• Group delay function: also known as GDF, it is defined as the negative derivative of the unwrapped phase of the signal Fourier transform (see Yegnanarayana and Murthy [80]) and reveals information about temporal localization of events (i.e., signal peaks). This feature has been used for determining the instants of significant excitation in speech signals (like in Smits and Yegnanarayana [81], or Rao et al [82]) and in beat identification in music performances (Sethares et al [83]).…”

Section: Stft-based Frequency Featuresmentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

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Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

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Significance of group delay functions in spectrum estimation

Cited by 111 publications

References 6 publications

Spoofing detection from a feature representation perspective

Spoofing detection from a feature representation perspective

Novel energy separation based instantaneous frequency features for spoof speech detection

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

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