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Discrete cosine transform-based data hiding for speech bandwidth extension

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Abstract

The limited narrow frequency range of 300–3400 Hz used in public switched telephone networks causes a significant reduction of speech quality. To address this drawback, a new robust transform-domain speech bandwidth extension method is proposed in this paper. The method uses the discrete Cosine transform-based data hiding (DCTBDH) technique to provide a better-quality wideband speech signal. The spectral envelope parameters are extracted from the high-frequency components of speech signal existing above narrowband, which are then spread by using spreading sequences, and are embedded within the DCT coefficients of narrowband signal. A better-quality wideband signal is reconstructed using the extracted embedded information at the receiver end. In simulations, the high-quality wideband speech was obtained from speech transmitted over a public switched telephone network. The spectral envelope parameters of the high-frequency components of the speech signal are transparently embedded with a mean square error of 5.78 × 10–4. In a mean opinion score (MOS) listening test, we verified that the proposed method yields improved perceptual transparency compared to conventional methods of about 0.21 points on the MOS scale. The log spectral distortion value obtained was 2.2248 which showed that the proposed technique yields an improved quality of speech signal compared to conventional methods.

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References

  • Abel, J., & Fingscheidt, T. (2017). A DNN Regression Approach to Speech Enhancement by Artificial Bandwidth Extension. In Proceedings of IEEE workshop on applications of signal processing to audio and acoustics, pp. 219–223.

  • Archit, G., Brendan, S., Yannis, A. & Thomas, C. W. (2019). Speech bandwidth extension with wavenet. In Proceedings of IEEE workshop on applications of signal processing to audio and acoustics, pp. 205–208.

  • Berthy, F., Zeyu, J., Jiaqi, S., & Adam, F. (2019). Learning bandwidth expansion using perceptually-motivated loss. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 606–610.

  • Bhatt, N., & Kosta, Y. (2015). A novel approach for artificial bandwidth extension of speech signals by LPC technique over proposed GSM FR NB coder using high band feature extraction and various extension of excitation methods. International Journal of Speech Technology, 18(1), 57–64.

    Article  Google Scholar 

  • Bong-Ki, L., Kyoungjin, N., Joon-Hyuk, C., Kihyun, Ch., & Eunmi, O. (2018). Sequential deep neural networks ensemble for speech bandwidth extension. IEEE Access, 6, 27039–27047.

    Article  Google Scholar 

  • Chen, S., & Leung, H. (2005). Artificial bandwidth extension of telephony speech by data hiding. In Proceedings of International Symposium on Circuits and Systems (ISCAS), pp. 3151–3154.

  • Chen, S., Leung, H., & Ding, H. (2007). Telephony speech enhancement by data hiding. IEEE Transactions on Instrumentation and Measurement, 56(1), 63–74.

    Article  Google Scholar 

  • Chen, S., & Leung, H. (2007). Speech bandwidth extension by data hiding and phonetic classification. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 593–596.

  • Chen, Z., Zhao, C., Geng, G., & Yin, F. (2013). An audio watermark based speech bandwidth extension method. EURASIP Journal Audio, Speech and Music Processing, 10, 1–8.

    Google Scholar 

  • Dinan, E. H., & Jabbari, E. H. (1998). Spreading codes for direct sequence CDMA and wideband CDMA cellular networks. IEEE Communications Magazine, 36(9), 48–54.

    Article  Google Scholar 

  • ETSI ES 201 108 V1.1.2 (2000). Speech Processing, Transmission and Quality Aspects (STQ); Distributed speech recognition; Front-end feature extraction algorithm; Compression algorithms.

  • Garofalo, J. S., Lamel, L. F., & Fisher, W. M. (2013). Getting started with the DARPA TIMIT CD-ROM: An acoustic phonetic continuous speech database, National Institute of Standards and Technology (NIST).

  • Geiser, B., Jax, P., & Vary, P. (2005). Artificial bandwidth extension of speech supported by watermark-transmitted side information. In Proceedings of the 9th European Conference on Speech Communication and Technology, pp. 1497–1500.

  • Geiser, B., & Vary, P. (2007). Backwards compatible wideband telephony in mobile networks: CELP watermarking and bandwidth extension. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 533–536.

  • Geiser, B., & Vary, P. (2013). Speech bandwidth extension based on in-band transmission of higher frequencies. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 7507–7511.

  • Goldsmith, A. (2006). Wireless communications. Cambridge University Press.

    Book  Google Scholar 

  • Hanzo, L. L., Somerville, F. C. A., & Woodard, J. P. (2001). Voice compression and communications: Principles and applications for fixed and wireless channels. Wiley.

    Book  Google Scholar 

  • Hassan, A., Hershey, J. E., & Saulnier, G. J. (1998). Perspectives in spread spectrum. Kluwer Academic Publishers.

    Book  Google Scholar 

  • ITU-T. (2001). ITU-T Rec. P.862: Perceptual evaluation of speech quality (PESQ): An objective method for end to-end speech quality assessment of narrow-band telephone networks and speech codecs.

  • ITU-T. (2005). Recommendation P.862.2: Wideband extension to recommendation P.862 for the assessment of wideband telephone networks and speech codecs.

  • Jax, P. (2002). Enhancement of bandlimited speech signals: Algorithms and theoretical bounds. Ph.D. dissertation, RWTH Aachen University, Aachen, Germany.

  • Jax, P., & Vary, P. (2002). An upper bound on the quality of artificial bandwidth extension of narrowband speech signals. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 237–240.

  • Jax, P., & Vary, P. (2003). On artificial bandwidth extension of telephone speech. Signal Processing, 83(8), 1707–1719.

    Article  Google Scholar 

  • Jax, P., & Vary, P. (2006). Bandwidth extension of speech signals: A catalyst for the introduction of wideband speech coding? IEEE Communication Magazine, 44(5), 106–111.

    Article  Google Scholar 

  • Johannes, A., & Tim, F. (2019). Sinusoidal-based lowband synthesis for artificial speech bandwidth extension. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(4), 765–776.

    Article  Google Scholar 

  • Jonas, S., Friedrich, F., Markus, B., & Gerhard, S. (2019). Artificial bandwidth extension using a conditional generative adversarial network with discriminative training. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP) (pp. 7005–7009).

  • Kanhe, A., & Aghila, G. (2016). DCT based Audio Steganography in Voiced and Un-voiced Frames. In Proceedings of International Conference of Information and Analytics, pp. 1–4.

  • Keiser, B. E., & Strange, E. (1995). Digital telephony and network integration. Van Nostrand Reinhold.

    Book  Google Scholar 

  • Kosta, Y. (2016). Simulation and overall comparative evaluation of performance between different techniques for high band feature extraction based on artificial bandwidth extension of speech over proposed global system for mobile full rate narrow band coder. International Journal of Speech Technology, 19(4), 881–893.

    Article  Google Scholar 

  • Kyoungjin, N., & Joon-Hyuk, Ch. (2020). Deep neural network ensemble for reducing artificial noise in bandwidth extension. Digital Signal Processing, 102, 1–6.

    Google Scholar 

  • Mathieu, L., & Felix, G. (2020). Bandwidth extension of musical audio signals with no side information using dilated convolutional neural networks. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 801–805.

  • Nilsson, M., & Kleijn. W. B. (2001). Avoiding overestimation in bandwidth extension of telephony speech. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 869–872.

  • Pramod, B., Massimiliano, T., & Nicholas, E. (2019). Latent representation learning for artificial bandwidth extension using a conditional variational auto-encoder. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 7010–7014.

  • Prasad, N., & Kishore Kumar, T. (2016). Bandwidth extension of speech signals: A comprehensive review. International Journal of Intelligent Systems and Applications, 8(2), 45–52.

    Article  Google Scholar 

  • Prasad, N., & Kishore Kumar, T. (2017). Speech bandwidth extension aided by spectral magnitude data hiding. Circuits, Systems, and Signal Processing, 36(11), 4512–4540.

    Article  Google Scholar 

  • Sagi, A., & Malah, D. (2007). Bandwidth extension of telephone speech aided by data embedding. EURASIP Journal on Advances in Signal Processing, 2007, 37–52.

    MATH  Google Scholar 

  • Sunil Kumar, K., & Kishore Kumar, T. (2019). Speech Bandwidth Extension Aided by Hybrid Model Transform Domain Data Hiding. In Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5.

  • Xiang, H., Chenglin, X., Nana, H., Lei, X., EngSiong, Ch., & Haizhou, L. (2020). Time-domain neural network approach for speech bandwidth extension. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 866–870.

  • Yingwue, W., Shenghui, Z., & Dan, Q., (2016). Using conditional restricted Boltzmann machines for spectral envelope modelling in speech bandwidth extension. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 5930–5934.

  • Yuanjie, D., Yaxing, L., Xiaoqi, L., Shan, X., Dan, W., Zhihui, Z., & Shengwu, X. (2020). A time-frequency network with channel attention and non-local modules for artificial bandwidth extension. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp. 6954–6958.

  • Zhen-Hua, L., Yang, A., & Yu, G. (2018). Waveform modelling and generation using hierarchical recurrent neural networks for speech bandwidth extension. IEEE/ACM Transaction Audio, Speech, and Language Process, 26(5), 883–894.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal, India

    Sunil Kumar Koduri & Kishore Kumar T

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  1. Sunil Kumar Koduri
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  2. Kishore Kumar T
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Correspondence to Sunil Kumar Koduri.

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Koduri, S., T, K. Discrete cosine transform-based data hiding for speech bandwidth extension. Int J Speech Technol 25, 697–706 (2022). https://doi.org/10.1007/s10772-022-09980-x

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