Conference proceeding
Deep Neural Networks: A Case Study for Music Genre Classification
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp 655-660
Dec 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Music classification is a challenging problem with many applications in today's large-scale datasets with Gigabytes of music files and associated metadata and online streaming services. Recent success with deep neural network architectures on large-scale datasets has inspired numerous studies in the machine learning community for various pattern recognition and classification tasks such as automatic speech recognition, natural language processing, audio classification and computer vision. In this paper, we explore a two-layer neural network with manifold learning techniques for music genre classification. We compare the classification accuracy rate of deep neural networks with a set of well-known learning models including support vector machines (SVM and '1-SVM), logistic regression and '1-regression in combination with hand-crafted audio features for a genre classification task on a public dataset. Our experimental results show that neural networks are comparable with classic learning models when the data is represented in a rich feature space.
Metrics
Details
- Title
- Deep Neural Networks: A Case Study for Music Genre Classification
- Creators
- Arjun Raj Rajanna - Rochester Institute of TechnologyKamelia Aryafar - Drexel UniversityAli Shokoufandeh - Drexel UniversityRaymond Ptucha - Rochester Institute of TechnologyIEEE
- Publication Details
- 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp 655-660
- Conference
- 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 14th
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000380483600117
- Scopus ID
- 2-s2.0-84969701506
- Other Identifier
- 991019168564404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics