On the Impact of Deep Learning and Feature Extraction for Arabic Audio Classification and Speaker Identification
Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, ISSN: 2161-5330, Vol: 2022-December, Page: 1-8
2022
- 2Citations
- 3Usage
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations2
- Citation Indexes2
- CrossRef1
- Usage3
- Abstract Views3
- Captures4
- Readers4
Conference Paper Description
In recent times, machine learning and deep learning algorithms have contributed to the advances in audio and speech recognition. Despite the progress, there is limited emphasis on the classification of cantillation audio using deep learning. This paper introduces a dataset containing two labeled styles of cantillation from six reciters. Deep learning architectures including convolutional neural networks (CNN) and deep artificial neural networks (ANN) were used to classify the recitation styles using various spectrogram features. Moreover, the classification of the six reciters was also performed using deep learning. The best performance was achieved using a CNN model and Mel spectrograms resulting in an F1-score of 0.99 on the test set for classifying recitation style and an F1-score of 1.00 on the test set for classifying reciters. The results obtained in this work outperform the existing works in the literature. The paper also discusses the impact of various audio features and deep learning algorithms that apply to audio genre and speaker identification tasks.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85147001909&origin=inward; http://dx.doi.org/10.1109/aiccsa56895.2022.10017889; https://ieeexplore.ieee.org/document/10017889/; https://zuscholars.zu.ac.ae/works/5628; https://zuscholars.zu.ac.ae/cgi/viewcontent.cgi?article=6660&context=works
Institute of Electrical and Electronics Engineers (IEEE)
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