Phase aware deep neural network for noise robust voice activity detection

Wang, Longbiao; Phapatanaburi, Khomdet; Go, Zeyan; Nakagawa, Seiichi; Iwahashi, Masahiro; Dang, Jianwu

doi:10.1109/icme.2017.8019414

Cited by 16 publications

(10 citation statements)

References 18 publications

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“…This is due to the fact that joint magnitude and phase information make the DNN training/testing more efficient. The tendency is similar to a speaker recognition task referring to [16]. In comparison to baseline GMM based-detection in Table 4, although the MP-aware DNN could not provide better performance due to the lack of resolution within low frequencies of the used feature, it may be useful when its scores are combined with the score of another classifier.…”

Section: 2 Results Based On Conventional and Mp-aware Dnn-based Dementioning

confidence: 71%

“…Recently, phase information has been proven to be crucial for many speech processing tasks [22]. In [13,16] DNN using phase features augmented with corresponding magnitude features was proposed as a multi-frame-dependent regression and classification task that could improve the performance of DNN using only magnitude feature. This can be seen in the simultaneous improvement of joint phase and magnitude feature.…”

Section: Conventional and Mp-aware Dnn-based Detectionmentioning

confidence: 99%

“…Similarly, the authors of [14][15] also applied MP-aware DNN to enhance the magnitude and phase of noisy speech by operating in the complex domain. In [16], MP-aware DNN was proposed as a speech classification based on noise robust voice activity detection. The result indicated that the MP-aware DNN could reduce the equal error rate (EER) of the VAD due to additional phase introduction in the DNN training.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection

Phapatanaburi¹,

Buayai

Naktong

et al. 2020

ECTI-EEC

Self Cite

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Magnitude and phase aware deep neural network (MP aware DNN) based on Fast Fourier Transform information, has recently been received more attention to many speech applications. However, little attention has been paid to its aspect in terms of replay attack detection developed for the automatic speaker verification and countermeasures (ASVspoof 2017). This paper aims to investigate the MP aware DNN as a speech classification for detecting non-replayed (genuine) and replayed speech. Also, to exploit the advantage of the classifier-based complementary to improve the reliable detection decision, we propose a novel method by combining MP aware DNN with standard replay attack detection (that is, the use of constant Q transform cepstral coefficients-based Gaussian mixture model classification: CQCC-based GMM). Experiments are evaluated using ASVspoof 2017 and a standard measure of detection performance called equal error rate (EER). The results showed that MP aware DNN -based detection performed conventional DNN method using only the magnitude/phase features. Moreover, we found that score combination of CQCC-based GMM with MP aware DNN achieved additional improvement, indicating that MP aware DNN is very useful, especially when combined with the CQCC-based GMM for replay attack detection.

show abstract

Section: 2 Results Based On Conventional and Mp-aware Dnn-based Dementioning

confidence: 71%

Section: Conventional and Mp-aware Dnn-based Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection

Phapatanaburi¹,

Buayai

Naktong

et al. 2020

ECTI-EEC

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning has brought more robust methods for VAD. A deep neural network using magnitude and phase information was proposed for VAD under noise condition [7]. Convolutional neural networks have been used as acoustic models for VAD in noisy environments [8].…”

Section: Introductionmentioning

confidence: 99%

Rule-Embedded Network for Audio-Visual Voice Activity Detection in Live Musical Video Streams

Hou

Deng

Zhu³

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Detecting anchor's voice in live musical streams is an important preprocessing step for music and speech signal processing. Existing approaches to voice activity detection (VAD) primarily rely on audio, however, audio-based VAD is difficult to effectively focus on the target voice in noisy environments. This paper proposes a rule-embedded network to fuse the audio-visual (A-V) inputs for better detection of the target voice. The core role of the rule in the model is to coordinate the relation between the bi-modal information and use visual representations as a mask to filter out the information of non-target sound. Experiments show that: 1) with the help of cross-modal fusion using the proposed rule, the detection results of the A-V branch outperform that of the audio branch in the same model framework; 2) the performance of the bimodal A-V model far outperforms that of audio-only models, indicating that the incorporation of both audio and visual signals is highly beneficial for VAD. To attract more attention to the cross-modal music and audio signal processing, a new live musical video corpus with frame-level labels is introduced.

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“…Recently, the attention method was applied to small-footprint keyword recognition [10][11][12]. Various studies have also been conducted to improve recognition performance in noisy environments [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature

Yoon

Kim

2020

Electronics

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This paper proposes a small-footprint wake-up-word (WUW) recognition system for real noisy environments by employing the competing-words-based feature. Competing-words-based features are generated using a ResNet-based deep neural network with small parameters using the competing-words dataset. The competing-words dataset consists of the most acoustically similar and dissimilar words to the WUW used for our system. The obtained features are used as input to the classification network, which is developed using the convolutional neural network (CNN) model. To obtain sufficient data for training, data augmentation is performed by using a room impulse response filter and adding sound signals of various television shows as background noise, which simulates an actual living room environment. The experimental results demonstrate that the proposed WUW recognition system outperforms the baselines that employ CNN and ResNet models. The proposed system shows 1.31% in equal error rate and 1.40% false rejection rate at a 1.0% false alarm rate, which are 29.57% and 50.00% relative improvements compared to the ResNet system, respectively. The number of parameters used for the proposed system is reduced by 83.53% compared to the ResNet system. These results prove that the proposed system with the competing-words-based feature is highly effective at improving WUW recognition performance in noisy environments with a smaller footprint.

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Phase aware deep neural network for noise robust voice activity detection

Cited by 16 publications

References 18 publications

Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection

Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection

Rule-Embedded Network for Audio-Visual Voice Activity Detection in Live Musical Video Streams

Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature

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