The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
Mel Frequency Cepstral Coefficient and its Applications: A Review
Open accessAbstract
Feature extraction and representation has significant impact on the performance of any machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to model features of audio signal and is widely used in various fields. This paper aims to review the applications that the MFCC is used for in addition to some issues that facing the MFCC computation and its impact on the model performance. These issues include the use of MFCC for non-acoustic signals, adopting the MFCC alone or combining it with other features, the use of time series versus global representation of the MFCC, following the standard form of the MFCC computation versus modifying its parameters, and supplying the traditional machine learning methods versus the deep learning methods.
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Cites
Dimensionality Reduction: A Comparative Review
van der Maaten, L. J. P., Postma, E. O., & van den Herik, H. J. (2009) · Cited by 2,061
Machine learning for medical diagnosis: history, state of the art and perspective
Kononenko, I. (2001) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(01)00077-x · Cited by 1,306
A survey of feature selection and feature extraction techniques in machine learning
Khalid, S., Khalil, T., & Nasreen, S. (2014) · 2014 Science and Information Conference · doi:10.1109/sai.2014.6918213 · Cited by 1,157
Speech Recognition Using Deep Neural Networks: A Systematic Review
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,152
Comparison and evaluation of methods for liver segmentation from CT datasets
Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135
Power-spectrum analysis of three-dimensional redshift surveys
Feldman, H. A., Kaiser, N., & Peacock, J. A. (1994) · The Astrophysical Journal · doi:10.1086/174036 · Cited by 927
ECG-based heartbeat classification for arrhythmia detection: A survey
Luz, E. J. D. S., Schwartz, W. R., Cámara-Chávez, G., & Menotti, D. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.12.008 · Cited by 878
Muda, L., Begam, M. H., & Elamvazuthi, I. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1003.4083 · Cited by 803
Strang, G. (1999) · SIAM Review · doi:10.1137/s0036144598336745 · Cited by 788
Comparison of different implementations of MFCC
Zheng, F., Zhang, G., & Song, Z. (2001) · Journal of Computer Science and Technology · doi:10.1007/bf02943243 · Cited by 591
Cited by
A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
Deka, C., Shrivastava, A., Abraham, A. K., Nautiyal, S., & Chauhan, P. (2025) · Speech, Language and Hearing · doi:10.1080/2050571x.2024.2359274 · Cited by 44
Acoustic Features Analysis for Explainable Machine Learning-Based Audio Spoofing Detection
Bisogni, C., Loia, V., Nappi, M., & Pero, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104145 · Cited by 30
Metadata
- Authors
- Abdul, Z. K., & Al-Talabani, A. K.
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 122136-122158
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4313178214
- Cited by
- 527
- References
- 110
- FWCI
- 47.04
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
