7 citations · 12 across the 19 of their papers we have counts for
6 papers · 1 filter
Predicting User Intents and Musical Attributes from Music Discovery Conversations
Daeyong Kwon, SeungHeon Doh, Juhan Nam
Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains,…
Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models
SeungHeon Doh, Keunwoo Choi, Daeyong Kwon +2
A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system shou…
PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
Hayeon Bang, Eunjin Choi, Megan Finch +4
While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address…
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music Retrieval
SeungHeon Doh, Minhee Lee, Dasaem Jeong +1
Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challeng…
Musical Word Embedding for Music Tagging and Retrieval
SeungHeon Doh, Jongpil Lee, Dasaem Jeong +1
Word embedding has become an essential means for text-based information retrieval. Typically, word embeddings are learned from large quantities of general and unstructured text dat…
Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models
Taegyun Kwon, Dasaem Jeong, Juhan Nam
In recent years, advancements in neural network designs and the availability of large-scale labeled datasets have led to significant improvements in the accuracy of piano transcrip…