Details

Autor(en) / Beteiligte

• Rajanna, Arjun Raj
• Aryafar, Kamelia
• Shokoufandeh, Ali
• Ptucha, Raymond

Titel

Deep Neural Networks: A Case Study for Music Genre Classification

Ist Teil von

• 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015, p.655-660

Ort / Verlag

IEEE

Erscheinungsjahr

2015

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.