Data-driven audio recognition: a supervised dictionary approach

2020/12/29 by Imad Rida, Rida, Imad
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2012.14761

openalex publication_date 2020/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Machine hearing is an emerging area. Motivated by the need of a principled framework across domain applications for machine listening, we propose a generic and data-driven representation learning approach. For this sake, a novel and efficient supervised dictionary learning method is presented. Experiments are performed on both computational auditory scene (East Anglia and Rouen) and synthetic music chord recognition datasets. Obtained results show that our method is capable to reach state-of-the-art hand-crafted features for both applications

Citations

• Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
• Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
• rm K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• Representation Learning: A Review and New Perspectives
• Feature Extraction for Temporal Signal Recognition: An Overview

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