114 citations · 361 across the 18 of their papers we have counts for
6 papers · 1 filter
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures
Jongpil Lee, Taejun Kim, Jiyoung Park +1
Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics. However, as the image domain grows rapid…
Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Taejun Kim, Jongpil Lee, Juhan Nam
Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach…
Representation Learning of Music Using Artist Labels
Jiyoung Park, Jongpil Lee, Jangyeon Park +2
In music domain, feature learning has been conducted mainly in two ways: unsupervised learning based on sparse representations or supervised learning by semantic labels such as mus…
Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification
Jongpil Lee, Juhan Nam
Music tag words that describe music audio by text have different levels of abstraction. Taking this issue into account, we propose a music classification approach that aggregates m…
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging
Jongpil Lee, Juhan Nam
Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished fr…
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
Jongpil Lee, Jiyoung Park, Keunhyoung Luke Kim +1
Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text…