Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features by Rahman Shafique, Khadija Kanwal, Venkata Chunduri, Gyu Sang Choi, Imran Ashraf

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Implementation of Computer-Aided Piano Music Automatic Notation Algorithm in Psychological Detoxification by Xinmei Zhang

“…The short-time Fourier transform and constant Q-transform time-frequency analysis methods are implemented, respectively, and note onset detection and multibasic tone detection are implemented based on CNN models. …”
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A method of synthetic speech spoofing detection using constant Q modulation envelope by Jia XU, Zhihua JIAN, Honghui JIN, Chao WU

“…In response to the low accuracy of synthetic speech spoofing detection based on traditional acoustic feature parameters, poor detection performance for unknown types of synthetic speech, and performance degradation in noisy environments, a method for detecting spoofing synthetic speech was proposed using constant Q modulation envelope (CQME) .The motivation of the method was from the fact that the temporal envelope of speech contained abundant information and there was a big difference in detail between the envelope of synthetic speech and genuine speech.The modulation envelope spectrum of speech was obtained by employing constant Q transform (CQT), and the root mean square of each frequency component was calculated to derive the CQME feature vector.And then the CQME feature vector was used to train the random forest classifier for discriminating genuine speech from spoofing synthetic speech.Experimental results demonstrate that the random forest trained with CQME features achieves high detection performance on the ASVspoof 2019 dataset and exhibites good detection efficacy for unknown types of synthetic speech.Furthermore, the proposed method shows high detection performance even under various noise conditions, having excellent noise robustness.…”
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Vocal performance evaluation of the intelligent note recognition method based on deep learning by Dongyun Chang

“…The incorporation of multiple feature inputs can remarkably enhance the accuracy of vocal performance evaluation, especially the combination of constant Q Transform features, which is the most outstanding. …”
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EMVAS: end-to-end multimodal emotion visualization analysis system by Xianxun Zhu, Heyang Feng, Erik Cambria, Yao Huang, Ming Ju, Haochen Yuan, Rui Wang

“…For feature extraction, facial landmark detection and action unit analysis capture fine-grained visual cues; Mel-frequency cepstral coefficients, log-scaled fundamental frequency, and Constant-Q transform extract detailed audio features; and a Transformer-based encoder processes textual data for contextual emotion analysis. …”
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CASSAD: Chroma-Augmented Semi-Supervised Anomaly Detection for Conveyor Belt Idlers by Fahad Alharbi, Suhuai Luo, Abdullah Alsaedi, Sipei Zhao, Guang Yang

“…CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. …”
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Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features by Youxin Yu, Wenbo Zhu, Xiaoli Ma, Jialei Du, Yu Liu, Linhui Gan, Xiaoping An, Honghui Li, Buyu Wang, Xueliang Fu

“…The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms’ deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). …”
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Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution by Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Youngbae Jeon, Jeonghwan Hwang, Hyo-Jung Choi, Jong-Hoon Yang, So-Yul Han, Jun Ho Huh, Choong-Hoon Lee, Ji Won Yoon

“…Our single model achieved a replay attack detection equal error rate (EER) of 0.30% on the evaluation set using an optimized constant Q transform (CQT) feature, outperforming the top ensemble system in the competition, which scored an EER of 0.39%. …”
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