Deep Learning Based Speech Enhancement / (Record no. 603138)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01358nam a22001577a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | 0 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.382,MEH |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mehmood, Hamza |
| 245 ## - TITLE STATEMENT | |
| Title | Deep Learning Based Speech Enhancement / |
| Statement of responsibility, etc. | Capt Hamza Mehmood, Capt Muhammad Usman Hamid, Capt Muhammad Taimoor Waqas and Maj Intezar Ali. (BETE-56) |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | MCS, NUST |
| Name of producer, publisher, distributor, manufacturer | Rawalpindi |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 29 |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | This project focuses on exploring the effectiveness of deep learning systems in improving speech quality. The approach employs a fully attention-based mechanism that utilizes deep learning to enhance speech signals by processing noisy speech signals and producing perceptually enhanced clean speech signals. The model is trained on a large dataset of both noisy and clean speech signals and evaluated using both objective and subjective metrics on different benchmark datasets. Results show that the proposed method outperforms traditional speech enhancement techniques in terms of speech quality and intelligibility. The study also investigates the impact of various architectural and training parameters on the model's performance, demonstrating the potential of deep learning-based speech enhancement using Transformers-based forward feed models as a promising research area. |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor Dr. Abdul Wakeel |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| Military College of Signals (MCS) | Military College of Signals (MCS) | Reference | 09/23/2023 | 621.382,MEH | MCSPTE-327 | Project Report |