Significance of Constant-Q Transform for Voice Liveness Detection

Khoria, Kuldeep; Patil, Ankur T.; Patil, Hemant A.

doi:10.23919/eusipco54536.2021.9615993

Cited by 12 publications

(2 citation statements)

References 17 publications

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“…On the POCO dataset, the researchers did some work. Khoria et al proposed a new method for detecting voice liveness based on constant-Q transform (CQT) and SVM classifiers 12 . Singh et al investigated the efficacy of modified group delay functions (MGDF) features based on phase for detecting explosion noise.…”

Section: Related Workmentioning

confidence: 99%

FL-VLD: A Novel Voice Liveness Detection Framework Based on Asynchronous Federated Learning

Zong,

Li,

Zhu

et al. 2023

Preprint

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Voice Liveness Detection (VLD) has become one of the hot research topics in the Internet of Things era. Conventional VLD methods are centralized solutions that are trained on abundant data collected from local clients and stored on the server, however, they have the risk of causing data islands and privacy leakage. To address this problem, we propose a novel word-level VLD framework based on asynchronous federated learning (FL) with pop noise, named FL-VLD. Structurally, FL-VLD takes the preprocessed voice for local model training and constructs a global model by only transmitting the learned weights after differential privacy with FL’s central server in an asynchronous manner. In addition, the local network of the framework incorporates the residual network and the spatial grouping enhancement module to optimize the complexity and accuracy of the global model. With the advantage of FL’s distributed structure, FL-VLD solves the data island problem in VLD scenario without threatening users’ privacy. Experimental results on the popular POCO dataset show that our proposal is clearly superior to the traditional centralized methods as well as overperforming other federated schemes in terms of fairness, stability, accuracy, and lightweightness. Further, for attacks involving far-field replay, synthesis, and conversion, FL-VLD has high generalisation capabilities. Finally, the ablation study attests to its efficacy.

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Section: Related Workmentioning

confidence: 99%

FL-VLD: A Novel Voice Liveness Detection Framework Based on Asynchronous Federated Learning

Zong,

Li,

Zhu

et al. 2023

Preprint

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“…Thus, the CQT spectrogram represents the absolute pitch spectrum of sound. CQT spectrogram provides the benchmark results for classification of music [11] and detection of spoofing attacks [12]. From the CQT spectrogram, we compute the chroma-based spectrogram (i.e.…”

Section: Introductionmentioning

confidence: 99%

Improved speech emotion recognition based on music-related audio features

Phan

Han

et al. 2022

2022 30th European Signal Processing Conference (EUSIPCO)

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Emotions are essential for human communication as they reflect our inner states and influence our actions. Today, emotions provide crucial information to many applications, from virtual assistants to security systems, mood-tracking wearable devices, and autism robots. The speech emotion recognition (SER) model must be lightweight to run on varying devices with limited computational power. This research investigates the performance of music-related features for SER based on the auditory and neuropsychology evidence about the connection of emotional speech and music in human perception. Unlike prior works on low-level descriptors that primarily focus on differentiating human speech production, our method employs features extracted directly from raw speech signals through Discrete Fourier Transform and Constant-Q Transform. These features represent the perceptual pitches and timbre characteristics of the human voice. The 10-fold cross-validation results show that our method improves the accuracy of the audio feature-based approach on RAVDESS, CREMA-D and IEMOCAP datasets. Findings from the ablation study imply the significance of perceptual pitch, the perceptual loudness and the combination of pitch and timbre features in building a robust SER model. Compared to pretrained deep learning embeddings, our method demonstrates its generalizability and high efficiency despite a much smaller model size.

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Voice Liveness Detection Using Bump Wavelet with CNN

Gupta,

Patil

2024

Lecture Notes in Computer Science

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Significance of Constant-Q Transform for Voice Liveness Detection

Cited by 12 publications

References 17 publications

FL-VLD: A Novel Voice Liveness Detection Framework Based on Asynchronous Federated Learning

FL-VLD: A Novel Voice Liveness Detection Framework Based on Asynchronous Federated Learning

Improved speech emotion recognition based on music-related audio features

Voice Liveness Detection Using Bump Wavelet with CNN

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