{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/performance-analysis-of-hybrid-quantum","title":"Performance Analysis of Hybrid Quantum-Classical Convolutional Neural Networks for Audio Classification","arxiv_id":null,"date":"2024-11-04","proceeding":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) 2024 11","authors":["Yash Thakar","Bhuvi Ghosh","Vishma Adeshra","Kriti Srivastava"],"abstract":"Audio signals being high-dimensional and complex pose challenges for classical machine learning techniques in terms of computation and generalization on real-world data. This paper evaluates the use of hybrid quantum-classical convolutional neural networks (QC-CNN) that leverage quantum effects like superposition and entanglement for audio classification using mel-spectrograms obtained from audio data. Evaluated on both small-sized and large-sized datasets, the proposed QC-CNN model gave comparable training accuracy with classical CNN (Convolutional Neural Network) on the smaller dataset but outperformed classical CNN on test accuracy (95.04% vs 92.88%) for a larger birdsong dataset and reduced overfitting, thus highlighting the potential advantages of QC-CNNs for audio data. The QC-CNN exhibited higher cross-entropy loss in case of the small-sized dataset which was further significantly reduced when evaluated on the large-sized birdsong dataset. The work demonstrates the application of QC-CNN for audio classification.","url_abs":"https://ieeexplore.ieee.org/document/10725668","url_pdf":"https://ieeexplore.ieee.org/document/10725668","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"performance-analysis-of-hybrid-quantum","repo_url":"https://github.com/yashyaks/Audio-QC-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"audio-classification","task_name":"Audio Classification"},{"task_slug":"quantum-machine-learning","task_name":"Quantum Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}