DLR : Toward a deep learned rhythmic representation for music content analysis

2017/12/14 by Yeonwoo Jeong, Keunwoo Choi, Jeong, Yeonwoo +3
Computer Science · Neuroscience · #FOS: Computer and information sciences #Music Technology and Sound Studies #Music and Audio Processing #Neuroscience and Music Perception #Sound (cs.SD)

paper · pdf · doi:10.48550/arxiv.1712.05119

openalex publication_date 2017/12/14 · openalex created_date 2017/12/22 · openalex updated_date 2026/07/28

Abstract

In the use of deep neural networks, it is crucial to provide appropriate input representations for the network to learn from. In this paper, we propose an approach to learn a representation that focus on rhythmic representation which is named as DLR (Deep Learning Rhythmic representation). The proposed approach aims to learn DLR from the raw audio signal and use it for other music informatics tasks. A 1-dimensional convolutional network is utilised in the learning of DLR. In the experiment, we present the results from the source task and the target task as well as visualisations of DLRs. The results reveals that DLR provides compact rhythmic information which can be used on multi-tagging task.

Citations

• Adam: A Method for Stochastic Optimization
• Automatic tagging using deep convolutional neural networks
• Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
• A Tutorial on Deep Learning for Music Information Retrieval

Related

• One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
• Deep Layered Learning in MIR
• Deep Learning for MIR Tutorial
• A Tutorial on Deep Learning for Music Information Retrieval
• Deep Learning Techniques for Music Generation -- A Survey