114 citations · 476 across the 43 of their papers we have counts for
22 papers · 1 filter
LP-MusicCaps: LLM-Based Pseudo Music Captioning
SeungHeon Doh, Keunwoo Choi, Jongpil Lee +1
Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of la…
PrimaDNN': A Characteristics-aware DNN Customization for Singing Technique Detection
Yuya Yamamoto, Juhan Nam, Hiroko Terasawa
Professional vocalists modulate their voice timbre or pitch to make their vocal performance more expressive. Such fluctuations are called singing techniques. Automatic detection of…
A study of audio mixing methods for piano transcription in violin-piano ensembles
Hyemi Kim, Jiyun Park, Taegyun Kwon +2
While piano music transcription models have shown high performance for solo piano recordings, their performance degrades when applied to ensemble recordings. This study aims to ana…
A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription
Sangeon Yong, Li Su, Juhan Nam
Note-level automatic music transcription is one of the most representative music information retrieval (MIR) tasks and has been studied for various instruments to understand music.…
Textless Speech-to-Music Retrieval Using Emotion Similarity
SeungHeon Doh, Minz Won, Keunwoo Choi +1
We introduce a framework that recommends music based on the emotions of speech. In content creation and daily life, speech contains information about human emotions, which can be e…
Neural Vocoder Feature Estimation for Dry Singing Voice Separation
Jaekwon Im, Soonbeom Choi, Sangeon Yong +1
Singing voice separation (SVS) is a task that separates singing voice audio from its mixture with instrumental audio. Previous SVS studies have mainly employed the spectrogram mask…
Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers
Yuya Yamamoto, Juhan Nam, Hiroko Terasawa
In this paper, we focus on singing techniques within the scope of music information retrieval research. We investigate how singers use singing techniques using real-world recording…
YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations
Eunjin Choi, Yoonjin Chung, Seolhee Lee +3
Existing multi-instrumental datasets tend to be biased toward pop and classical music. In addition, they generally lack high-level annotations such as emotion tags. In this paper,…
Hi,KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
Taesu Kim, SeungHeon Doh, Gyunpyo Lee +3
Wake-up words (WUW) is a short sentence used to activate a speech recognition system to receive the user's speech input. WUW utterances include not only the lexical information for…
Deformable CNN and Imbalance-Aware Feature Learning for Singing Technique Classification
Yuya Yamamoto, Juhan Nam, Hiroko Terasawa
Singing techniques are used for expressive vocal performances by employing temporal fluctuations of the timbre, the pitch, and other components of the voice. Their classification i…
EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation
Hsiao-Tzu Hung, Joann Ching, Seungheon Doh +3
While there are many music datasets with emotion labels in the literature, they cannot be used for research on symbolic-domain music analysis or generation, as there are usually au…
PocketVAE: A Two-step Model for Groove Generation and Control
Kyungyun Lee, Wonil Kim, Juhan Nam
Creating a good drum track to imitate a skilled performer in digital audio workstations (DAWs) can be a time-consuming process, especially for those unfamiliar with drums. In this…
Metric Learning vs Classification for Disentangled Music Representation Learning
Jongpil Lee, Nicholas J. Bryan, Justin Salamon +2
Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two centr…
Learning a Joint Embedding Space of Monophonic and Mixed Music Signals for Singing Voice
Kyungyun Lee, Juhan Nam
Previous approaches in singer identification have used one of monophonic vocal tracks or mixed tracks containing multiple instruments, leaving a semantic gap between these two doma…
A Hybrid of Deep Audio Feature and i-vector for Artist Recognition
Jiyoung Park, Donghyun Kim, Jongpil Lee +2
Artist recognition is a task of modeling the artist's musical style. This problem is challenging because there is no clear standard. We propose a hybrid method of the generative mo…
Revisiting Singing Voice Detection: a Quantitative Review and the Future Outlook
Kyungyun Lee, Keunwoo Choi, Juhan Nam
Since the vocal component plays a crucial role in popular music, singing voice detection has been an active research topic in music information retrieval. Although several proposed…
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…
Audio-to-score alignment of piano music using RNN-based automatic music transcription
Taegyun Kwon, Dasaem Jeong, Juhan Nam
We propose a framework for audio-to-score alignment on piano performance that employs automatic music transcription (AMT) using neural networks. Even though the AMT result may cont…
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…
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…