Shuting Ge^1,2, Jin Ren^2,3,*, Yihua Shi⁴, Yujun Zhang¹, Shunzhi Yang², Jinfeng Yang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746

Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal… More >

Jianmao Xiao¹, Ridong Huang¹, Jiangyu Wang¹, Zhean Zhong¹, Chenyu Liu¹, Yuanlong Cao^1,*, Chuying Ouyang²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2815-2834, 2023, DOI:10.32604/csse.2023.039716

Abstract Audio copyright is a crucial issue in the music industry, as it protects the rights and interests of creators and distributors. This paper studies the current situation of digital music copyright certification and proposes a music copyright certification framework based on “blockchain + edge computing mode,” abbreviated as MBE, by integrating edge computing into the Hyperledger Fabric system. MBE framework compresses and splits the audio into small chunks, performs Fast Fourier Transform (FFT) to extract the peak points of each frequency and combines them to obtain unique audio fingerprint information. After being confirmed by various nodes on the Fabric alliance… More >

Haifeng Lin^1,2, Xiangfeng Liu², Chen Chen², Zhibo Liu², Dexin Zhao³, Yiwen Zhang⁴, Weizhuang Li⁴, Mingsheng Cao^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 939-956, 2024, DOI:10.32604/cmes.2023.028656

Abstract Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things (IoT). In this paper, a device management system is proposed to track the devices by using audio-based location distinction techniques. In the proposed scheme, traditional cryptographic techniques, such as symmetric encryption algorithm, RSA-based signcryption scheme, and audio-based secure transmission, are utilized to provide authentication, non-repudiation, and confidentiality in the information interaction of the management system. Moreover, an audio-based location distinction method is designed to detect the position change of the devices. Specifically, the audio frequency response (AFR) of several… More >

Jiangchuan Li, Peisong He^*, Jiayong Liu, Jie Luo, Qiang Xia

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4893-4915, 2023, DOI:10.32604/cmc.2023.036819

Abstract Steganography techniques, such as audio steganography, have been widely used in covert communication. However, the deep neural network, especially the convolutional neural network (CNN), has greatly threatened the security of audio steganography. Besides, existing adversarial attacks-based countermeasures cannot provide general perturbation, and the transferability against unknown steganography detection methods is weak. This paper proposes a cover enhancement method for audio steganography based on universal adversarial perturbations with sample diversification to address these issues. Universal adversarial perturbation is constructed by iteratively optimizing adversarial perturbation, which applies adversarial attack techniques, such as Deepfool. Moreover, the sample diversification strategy is designed to improve… More >

Senquan Yang¹, Fan Ding¹, Jianjun Liu¹, Pu Li^1,2, Songxi Hu^1,2,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3309-3323, 2023, DOI:10.32604/iasc.2023.035051

Abstract Audio signal separation is an open and challenging issue in the classical “Cocktail Party Problem”. Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix is derived using Newton gradient… More >

Alaknanda S. Patil^1,*, G. Sundari¹, Arun Kumar Sivaraman²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1707-1721, 2023, DOI:10.32604/csse.2023.034270

Abstract The intact data transmission to the authentic user is becoming crucial at every moment in the current era. Steganography; is a technique for concealing the hidden message in any cover media such as image, video; and audio to increase the protection of data. The resilience and imperceptibility are improved by choosing an appropriate embedding position. This paper gives a novel system to immerse the secret information in different videos with different methods. An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image. A hidden message is first included… More >

Abdullah A Asiri¹, Akhtar Badshah², Fazal Muhammad^3,*, Hassan A Alshamrani¹, Khalil Ullah⁴, Khalaf A Alshamrani¹, Samar Alqhtani⁵, Muhammad Irfan⁶, Hanan Talal Halawani⁷, Khlood M Mehdar⁸

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5075-5089, 2022, DOI:10.32604/cmc.2022.031156

Abstract Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response to audiovisual stimuli for the… More >

B. Jaishankar^1,*, Raghunathan Anitha², Finney Daniel Shadrach¹, M. Sivarathinabala³, V. Balamurugan⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1823-1836, 2023, DOI:10.32604/csse.2023.022938

Abstract In the discipline of Music Information Retrieval (MIR), categorizing music files according to their genre is a difficult process. Music genre classification is an important multimedia research domain for classification of music databases. In the proposed method music genre classification using features obtained from audio data is proposed. The classification is done using features extracted from the audio data of popular online repository namely GTZAN, ISMIR 2004 and Latin Music Dataset (LMD). The features highlight the differences between different musical styles. In the proposed method, feature selection is performed using an African Buffalo Optimization (ABO), and the resulting features are… More >

Noureen Fatima¹, Rashid Jahangir², Ghulam Mujtaba¹, Adnan Akhunzada^3,*, Zahid Hussain Shaikh⁴, Faiza Qureshi¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4357-4374, 2022, DOI:10.32604/cmc.2022.023830

Abstract The Coronavirus Disease 2019 (COVID-19) pandemic poses the worldwide challenges surpassing the boundaries of country, religion, race, and economy. The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) testing. Nevertheless, this testing method is accurate enough for the diagnosis of COVID-19. However, it is time-consuming, expensive, expert-dependent, and violates social distancing. In this paper, this research proposed an effective multi-modality-based and feature fusion-based (MMFF) COVID-19 detection technique through deep neural networks. In multi-modality, we have utilized the cough samples, breathe samples and sound samples of healthy as well as COVID-19 patients from… More >

Osama S. Faragallah^1,*, Mahmoud Farouk², Hala S. El-Sayed³

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3025-3037, 2022, DOI:10.32604/cmc.2022.019951

Abstract This paper answers three essential questions for audio speech cryptosystems in time and discrete transform domains. The first question is, what are the best values of sub-keys that must be used to get the best quality and security for the audio cryptosystem in time and discrete transform domains. The second question is the relation between the number of sub-keys, the number of secret keys used, and the audio speech signal block’s size. Finally, how many possible secret keys can be used to get the best quality and security results for the audio speech cryptosystem in time and discrete transform domains.… More >