Search Results for author: Juhan Nam

Found 40 papers, 22 papers with code

Expressive Acoustic Guitar Sound Synthesis with an Instrument-Specific Input Representation and Diffusion Outpainting

no code implementations24 Jan 2024 Hounsu Kim, Soonbeom Choi, Juhan Nam

Synthesizing performing guitar sound is a highly challenging task due to the polyphony and high variability in expression.

A Real-Time Lyrics Alignment System Using Chroma And Phonetic Features For Classical Vocal Performance

no code implementations17 Jan 2024 Jiyun Park, Sangeon Yong, Taegyun Kwon, Juhan Nam

The goal of real-time lyrics alignment is to take live singing audio as input and to pinpoint the exact position within given lyrics on the fly.

T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis

no code implementations17 Jan 2024 Yoonjin Chung, Junwon Lee, Juhan Nam

T-Foley generates high-quality audio using two conditions: the sound class and temporal event feature.

The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation

1 code implementation16 Nov 2023 Ilaria Manco, Benno Weck, Seungheon Doh, Minz Won, Yixiao Zhang, Dmitry Bogdanov, Yusong Wu, Ke Chen, Philip Tovstogan, Emmanouil Benetos, Elio Quinton, György Fazekas, Juhan Nam

We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models.

Music Captioning Music Generation +2

VoiceLDM: Text-to-Speech with Environmental Context

no code implementations24 Sep 2023 Yeonghyeon Lee, Inmo Yeon, Juhan Nam, Joon Son Chung

This paper presents VoiceLDM, a model designed to produce audio that accurately follows two distinct natural language text prompts: the description prompt and the content prompt.

AudioCaps

K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling

2 code implementations20 Sep 2023 Haven Kim, Jongmin Jung, Dasaem Jeong, Juhan Nam

To broaden the scope of genres and languages in lyric translation studies, we introduce a novel singable lyric translation dataset, approximately 89\% of which consists of K-pop song lyrics.

Translation

A Computational Evaluation Framework for Singable Lyric Translation

no code implementations26 Aug 2023 Haven Kim, Kento Watanabe, Masataka Goto, Juhan Nam

Lyric translation plays a pivotal role in amplifying the global resonance of music, bridging cultural divides, and fostering universal connections.

Semantic Similarity Semantic Textual Similarity +1

LP-MusicCaps: LLM-Based Pseudo Music Captioning

1 code implementation31 Jul 2023 Seungheon Doh, Keunwoo Choi, Jongpil Lee, Juhan Nam

In addition, we trained a transformer-based music captioning model with the dataset and evaluated it under zero-shot and transfer-learning settings.

Language Modelling Large Language Model +3

PrimaDNN': A Characteristics-aware DNN Customization for Singing Technique Detection

no code implementations25 Jun 2023 Yuya Yamamoto, Juhan Nam, Hiroko Terasawa

Automatic detection of singing techniques from audio tracks can be beneficial to understand how each singer expresses the performance, yet it can also be difficult due to the wide variety of the singing techniques.

A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription

no code implementations12 Apr 2023 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.

Information Retrieval Music Information Retrieval +2

Textless Speech-to-Music Retrieval Using Emotion Similarity

no code implementations19 Mar 2023 Seungheon Doh, Minz Won, Keunwoo Choi, Juhan Nam

We introduce a framework that recommends music based on the emotions of speech.

Retrieval

Music Playlist Title Generation Using Artist Information

1 code implementation14 Jan 2023 Haven Kim, Seungheon Doh, Junwon Lee, Juhan Nam

Automatically generating or captioning music playlist titles given a set of tracks is of significant interest in music streaming services as customized playlists are widely used in personalized music recommendation, and well-composed text titles attract users and help their music discovery.

Music Recommendation

Toward Universal Text-to-Music Retrieval

3 code implementations26 Nov 2022 Seungheon Doh, Minz Won, Keunwoo Choi, Juhan Nam

This paper introduces effective design choices for text-to-music retrieval systems.

Music Classification Retrieval +2

A Melody-Unsupervision Model for Singing Voice Synthesis

no code implementations13 Oct 2021 Soonbeom Choi, Juhan Nam

We also show that the proposed model is capable of being trained with speech audio and text labels but can generate singing voice in inference time.

Singing Voice Synthesis

Polyphonic Piano Transcription Using Autoregressive Multi-State Note Model

no code implementations2 Oct 2020 Taegyun Kwon, Dasaem Jeong, Juhan Nam

Recent advances in polyphonic piano transcription have been made primarily by a deliberate design of neural network architectures that detect different note states such as onset or sustain and model the temporal evolution of the states.

A Computational Analysis of Real-World DJ Mixes using Mix-To-Track Subsequence Alignment

1 code implementation24 Aug 2020 Taejun Kim, Minsuk Choi, Evan Sacks, Yi-Hsuan Yang, Juhan Nam

A DJ mix is a sequence of music tracks concatenated seamlessly, typically rendered for audiences in a live setting by a DJ on stage.

Disentangled Multidimensional Metric Learning for Music Similarity

no code implementations9 Aug 2020 Jongpil Lee, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, Juhan Nam

For this task, it is typically necessary to define a similarity metric to compare one recording to another.

Metric Learning Specificity +1

Metric Learning vs Classification for Disentangled Music Representation Learning

no code implementations9 Aug 2020 Jongpil Lee, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, Juhan Nam

For this, we (1) outline past work on the relationship between metric learning and classification, (2) extend this relationship to multi-label data by exploring three different learning approaches and their disentangled versions, and (3) evaluate all models on four tasks (training time, similarity retrieval, auto-tagging, and triplet prediction).

Classification Disentanglement +6

Musical Word Embedding: Bridging the Gap between Listening Contexts and Music

no code implementations23 Jul 2020 Seungheon Doh, Jongpil Lee, Tae Hong Park, Juhan Nam

Word embedding pioneered by Mikolov et al. is a staple technique for word representations in natural language processing (NLP) research which has also found popularity in music information retrieval tasks.

Information Retrieval Music Information Retrieval +1

VirtuosoNet: A Hierarchical RNN-based System for Modeling Expressive Piano Performance

1 code implementation ISMIR 2019 Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Kyogu Lee, Juhan Nam

In this paper, we present our application of deep neural network to modeling piano performance, which imitates the expressive control of tempo, dynamics, articulations and pedaling from pianists.

Music Performance Rendering

Temporal Feedback Convolutional Recurrent Neural Networks for Speech Command Recognition

1 code implementation30 Oct 2019 Taejun Kim, Juhan Nam

End-to-end learning models using raw waveforms as input have shown superior performances in many audio recognition tasks.

Keyword Spotting

Zero-shot Learning for Audio-based Music Classification and Tagging

1 code implementation5 Jul 2019 Jeong Choi, Jongpil Lee, Jiyoung Park, Juhan Nam

Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels.

Attribute Classification +5

Representation Learning of Music Using Artist, Album, and Track Information

no code implementations27 Jun 2019 Jongpil Lee, Jiyoung Park, Juhan Nam

Supervised music representation learning has been performed mainly using semantic labels such as music genres.

Representation Learning

Learning a Joint Embedding Space of Monophonic and Mixed Music Signals for Singing Voice

1 code implementation26 Jun 2019 Kyungyun Lee, Juhan Nam

We show the effectiveness of our system for singer identification and query-by-singer in both the same-domain and cross-domain tasks.

Sound Audio and Speech Processing

Zero-shot Learning and Knowledge Transfer in Music Classification and Tagging

no code implementations20 Jun 2019 Jeong Choi, Jongpil Lee, Jiyoung Park, Juhan Nam

Music classification and tagging is conducted through categorical supervised learning with a fixed set of labels.

Classification General Classification +3

Deep Content-User Embedding Model for Music Recommendation

1 code implementation18 Jul 2018 Jongpil Lee, Kyungyun Lee, Jiyoung Park, Jang-Yeon Park, Juhan Nam

Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach.

Collaborative Filtering Music Auto-Tagging +2

Revisiting Singing Voice Detection: a Quantitative Review and the Future Outlook

4 code implementations4 Jun 2018 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.

Information Retrieval Music Information Retrieval +1

Raw Waveform-based Audio Classification Using Sample-level CNN Architectures

no code implementations4 Dec 2017 Jongpil Lee, Taejun Kim, Jiyoung Park, Juhan Nam

Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics.

Audio Classification General Classification +1

Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms

2 code implementations28 Oct 2017 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.

General Classification Music Auto-Tagging

Representation Learning of Music Using Artist Labels

2 code implementations18 Oct 2017 Jiyoung Park, Jongpil Lee, Jangyeon Park, Jung-Woo Ha, Juhan Nam

In this paper, we present a supervised feature learning approach using artist labels annotated in every single track as objective meta data.

Sound Audio and Speech Processing

Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms

3 code implementations6 Mar 2017 Jongpil Lee, Jiyoung Park, Keunhyoung Luke Kim, Juhan Nam

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 and speech domains.

Music Auto-Tagging Music Classification

Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging

1 code implementation6 Mar 2017 Jongpil Lee, Juhan Nam

Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip.

General Classification Image Classification +2

A Deep Bag-of-Features Model for Music Auto-Tagging

1 code implementation20 Aug 2015 Juhan Nam, Jorge Herrera, Kyogu Lee

Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms.

Audio Classification Information Retrieval +4

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