خواندن و پیاده سازی یک مقاله و اگر نیاز بشه کمک به فهم مقاله برای ارائه در حوزه dsp

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شرح پروژه

بسم الله

کلیت پروژه خواندن و پیاده سازی یک مقاله و احتمالا اگر نیاز بشه کمک به فهم مقاله برای ارائه هست در حوزه dsp

در ادامه جزییات بیشتر کار که توسط استاد گفته شده بهمراه لیست مقالاتی که مثلا ترم های گذشته مورد تایید بودن و این ترم نمیشه استفاده کرد موجود هست تا حس بهتری نسبت به کاری که قرار هست انجام بشه دستتون بیاد.

تحويل پروژه شامل ارايه فايل گزارش کتبي و اجراي کدهاي آن نيز براي هفته چهارم بهمن ماه در نظر گرفته شده است.  

در ادامه عناوين برخي از ژورنالها و کنفرانسهاي معتبر در حوزه پردازش سیگنال آورده شده است: 

- IEEE Transactions on Signal Processing

- IEEE Signal Processing Magazine

- IEEE Signal Processing Letters

- Signal Processing

- Digital Signal Processing: A Review Journal

- Biomedical Signal Processing and Control

- Circuits, Systems, and Signal Processing

- Journal of Signal Processing Systems

- Signal, Image and Video Processing

- IET Signal Processing

- Multidimensional Systems and Signal Processing

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

ليست مقالاتي که در ترمهاي گذشته براي بخش سمينار و پروژه درس توسط دانشجويان انتخاب و ارايه شده است و قابل پذيرش براي ترم جاري نمي باشند:
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Narendra, N. P., & Alku, P. (2020). Glottal source information for pathological voice detection. IEEE Access, 8, 67745-67755.

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Nercessian, S., Sarroff, A., & Werner, K. J. (2021, June). Lightweight and interpretable neural modeling of an audio distortion effect using hyper conditioned differentiable biquads. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 890-894). 

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Ankışhan, H. (2020). Blood pressure prediction from speech recordings. Biomedical Signal Processing and Control, 58, 101842

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Li, Q., Shen, L., Guo, S., & Lai, Z. (2021). WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification. IEEE Transactions on Image Processing, 30, 7074-7089.

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Vlasenko, B., Sagha, H., Cummins, N., Schuller, B. (2017) Implementing Gender-Dependent Vowel-Level Analysis for Boosting Speech-Based Depression Recognition. Proc. Interspeech 2017, 3266-3270.

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Capizzi, G., Coco, S., Sciuto, G. L., & Napoli, C. (2018). A new iterative FIR filter design approach using a Gaussian approximation. IEEE Signal Processing Letters, 25(11), 1615-1619.

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Hamidi, M., Ghassemian, H., & Imani, M. (2018). Classification of heart sound signal using curve fitting and fractal dimension. Biomedical Signal Processing and Control, 39, 351-359.

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 Ledesma, S., Ibarra-Manzano, M. A., Almanza-Ojeda, D. L., & Cabal-Yepez, E. (2018). Cost function to analyze the passband and the stopband of Digital Biquadratic Filters. Digital Signal Processing, 75, 255-263.

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Ma, W., Pan, Z., Guo, J., & Lei, B. (2019). Achieving super-resolution remote sensing images via the wavelet transform combined with the recursive res-net. IEEE Transactions on Geoscience and Remote Sensing, 57(6), 3512-3527.

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Salau, A. O., Oluwafemi, I., Faleye, K. F., & Jain, S. (2019, March). Audio compression using a modified discrete cosine transform with temporal auditory masking. In 2019 International Conference on Signal Processing and Communication (ICSC) (pp. 135-142). IEEE.

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Kumar, K., Kumar, R., de Boissiere, T., Gestin, L., Teoh, W. Z., Sotelo, J., ... & Courville, A. (2019). Melgan: Generative adversarial networks for conditional waveform synthesis. arXiv preprint arXiv:1910.06711.

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Colonnese, S., Markopoulos, P. P., Scarano, G., & Pados, D. A. (2021). FFT calculation of the L1-norm principal component of a data matrix. Signal Processing, 189, 108286.

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Engel, J., Hantrakul, L., Gu, C., & Roberts, A. (2020). DDSP: Differentiable digital signal processing. arXiv preprint arXiv:2001.04643.

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Bouallegue, G., Djemal, R., Alshebeili, S. A., & Aldhalaan, H. (2020). A dynamic filtering DF-RNN deep-learning-based approach for EEG-based neurological disorders diagnosis. IEEE Access, 8, 206992-207007.

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 Ravanelli, M., & Bengio, Y. (2018). Speech and speaker recognition from raw waveform with sincnet. arXiv preprint arXiv:1812.05920.

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Fainberg, J., Klejch, O., Loweimi, E., Bell, P., & Renals, S. (2019, December). Acoustic model adaptation from raw waveforms with SincNet. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (pp. 897-904). IEEE.

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Sraitih, M., & Jabrane, Y. (2021). A denoising performance comparison based on ECG Signal Decomposition and local means filtering. Biomedical Signal Processing and Control, 69, 102903.

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Lin Jr, J., Li, Y. P., Hsu, W. C., & Lee, T. S. (2016). Design of an FMCW radar baseband signal processing system for automotive application. SpringerPlus, 5(1), 1-16.

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Pilipović, R., Risojević, V., & Bulić, P. (2021). On the Design of an Energy Efficient Digital IIR A-Weighting Filter Using Approximate Multiplication. Sensors, 21(3), 732.

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Zeng, H., Wu, Z., Zhang, J., Yang, C., Zhang, H., Dai, G., & Kong, W. (2019). EEG emotion classification using an improved SincNet-based deep learning model. Brain sciences, 9(11), 326.

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Parno, B., Howell, J., Gentry, C., & Raykova, M. (2013, May). Pinocchio: Nearly practical verifiable computation. In 2013 IEEE Symposium on Security and Privacy (pp. 238-252). IEEE.

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Cicone, A., Liu, J., & Zhou, H. (2016). Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis. Applied and Computational Harmonic Analysis, 41(2), 384-411.

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Ullah, A., Anwar, S. M., Bilal, M., & Mehmood, R. M. (2020). Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation. Remote Sensing, 12(10), 1685.

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Gao, L., Qi, L., & Guan, L. (2021). The property of frequency shift in 2D-FRFT domain with application to image encryption. IEEE Signal Processing Letters, 28, 185-189.

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 Romero, F. P., Romaguera, L. V., Vázquez-Seisdedos, C. R., Costa, M. G. F., & Neto, J. E. (2018). Baseline wander removal methods for ECG signals: A comparative study. arXiv preprint arXiv:1807.11359.

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 Xing, Y., Qian, Z., & Chen, Q. (2021). Invertible image signal processing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6287-6296).

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 Nunes, J. A. C., Macêdo, D., & Zanchettin, C. (2019, July). Additive margin sincnet for speaker recognition. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-5). IEEE.

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 Kuznetsov, B., Parker, J. D., & Esqueda, F. (2020, September). Differentiable IIR filters for machine learning applications. In Proc. Int. Conf. Digital Audio Effects (eDAFx-20) (pp. 297-303).

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 Loweimi, E., Yue, Z., Bell, P., Renals, S., & Cvetkovic, Z. (2023). Multi-Stream Acoustic Modelling Using Raw Real and Imaginary Parts of the Fourier Transform. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 876-890.

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 Alías, F., Socoró, J. C., & Sevillano, X. (2016). A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds. Applied Sciences, 6(5), 143.

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 Anand, A., Negi, S., & Narendra, N. (2021, December). Filters Know How You Feel: Explaining Intermediate Speech Emotion Classification Representations. In 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 756-761). IEEE.

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