224 citations · 767 across the 33 of their papers we have counts for
35 papers
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…
HCLAS-X: Hierarchical and Cascaded Lyrics Alignment System Using Multimodal Cross-Correlation
Minsung Kang, Soochul Park, Keunwoo Choi
In this work, we address the challenge of lyrics alignment, which involves aligning the lyrics and vocal components of songs. This problem requires the alignment of two distinct mo…
A Demand-Driven Perspective on Generative Audio AI
Sangshin Oh, Minsung Kang, Hyeongi Moon +2
To achieve successful deployment of AI research, it is crucial to understand the demands of the industry. In this paper, we present the results of a survey conducted with professio…
Room Impulse Response Estimation in a Multiple Source Environment
Kyungyun Lee, Jeonghun Seo, Keunwoo Choi +2
In real-world acoustic scenarios, there often are multiple sound sources present in a room. These sources are situated in various locations and produce sounds that reach the listen…
Foley Sound Synthesis at the DCASE 2023 Challenge
Keunwoo Choi, Jaekwon Im, Laurie Heller +5
The addition of Foley sound effects during post-production is a common technique used to enhance the perceived acoustic properties of multimedia content. Traditionally, Foley sound…
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…
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training
Kin Wai Cheuk, Keunwoo Choi, Qiuqiang Kong +5
In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from…
Toward Universal Text-to-Music Retrieval
SeungHeon Doh, Minz Won, Keunwoo Choi +1
This paper introduces effective design choices for text-to-music retrieval systems. An ideal text-based retrieval system would support various input queries such as pre-defined tag…
MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
Chang-Bin Jeon, Hyeongi Moon, Keunwoo Choi +2
Separation of multiple singing voices into each voice is a rarely studied area in music source separation research. The absence of a benchmark dataset has hindered its progress. In…
A Proposal for Foley Sound Synthesis Challenge
Keunwoo Choi, Sangshin Oh, Minsung Kang +1
"Foley" refers to sound effects that are added to multimedia during post-production to enhance its perceived acoustic properties, e.g., by simulating the sounds of footsteps, ambie…
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications
Kin Wai Cheuk, Keunwoo Choi, Qiuqiang Kong +5
In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from…
Music Classification: Beyond Supervised Learning, Towards Real-world Applications
Minz Won, Janne Spijkervet, Keunwoo Choi
Music classification is a music information retrieval (MIR) task to classify music items to labels such as genre, mood, and instruments. It is also closely related to other concept…
Semi-Supervised Music Tagging Transformer
Minz Won, Keunwoo Choi, Xavier Serra
We present Music Tagging Transformer that is trained with a semi-supervised approach. The proposed model captures local acoustic characteristics in shallow convolutional layers, th…
SpecTNT: a Time-Frequency Transformer for Music Audio
Wei-Tsung Lu, Ju-Chiang Wang, Minz Won +2
Transformers have drawn attention in the MIR field for their remarkable performance shown in natural language processing and computer vision. However, prior works in the audio proc…
Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation
Qiuqiang Kong, Yin Cao, Haohe Liu +2
Deep neural network based methods have been successfully applied to music source separation. They typically learn a mapping from a mixture spectrogram to a set of source spectrogra…
Listen, Read, and Identify: Multimodal Singing Language Identification of Music
Keunwoo Choi, Yuxuan Wang
We propose a multimodal singing language classification model that uses both audio content and textual metadata. LRID-Net, the proposed model, takes an audio signal and a language…
Large-Scale MIDI-based Composer Classification
Qiuqiang Kong, Keunwoo Choi, Yuxuan Wang
Music classification is a task to classify a music piece into labels such as genres or composers. We propose large-scale MIDI based composer classification systems using GiantMIDI-…
Deep Composer Classification Using Symbolic Representation
Sunghyeon Kim, Hyeyoon Lee, Sunjong Park +2
In this study, we train deep neural networks to classify composer on a symbolic domain. The model takes a two-channel two-dimensional input, i.e., onset and note activations of tim…
Dereverberation using joint estimation of dry speech signal and acoustic system
Sanna Wager, Keunwoo Choi, Simon Durand
The purpose of speech dereverberation is to remove quality-degrading effects of a time-invariant impulse response filter from the signal. In this report, we describe an approach to…
Deep Unsupervised Drum Transcription
Keunwoo Choi, Kyunghyun Cho
We introduce DrummerNet, a drum transcription system that is trained in an unsupervised manner. DrummerNet does not require any ground-truth transcription and, with the data-scalab…
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…
Similarity measures for vocal-based drum sample retrieval using deep convolutional auto-encoders
Adib Mehrabi, Keunwoo Choi, Simon Dixon +1
The expressive nature of the voice provides a powerful medium for communicating sonic ideas, motivating recent research on methods for query by vocalisation. Meanwhile, deep learni…
DLR : Toward a deep learned rhythmic representation for music content analysis
Yeonwoo Jeong, Keunwoo Choi, Hosan Jeong
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 repre…
A Tutorial on Deep Learning for Music Information Retrieval
Keunwoo Choi, György Fazekas, Kyunghyun Cho +1
Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the…