27 citations · 70 across the 22 of their papers we have counts for
6 papers · 1 filter
MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data
Ju-Chiang Wang, Jordan B. L. Smith, Yun-Ning Hung
Music structure analysis (MSA) systems aim to segment a song recording into non-overlapping sections with useful labels. Previous MSA systems typically predict abstract labels in a…
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
Yun-Ning Hung, Chao-Han Huck Yang, Pin-Yu Chen +1
Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often re…
Feature-informed Embedding Space Regularization For Audio Classification
Yun-Ning Hung, Alexander Lerch
Feature representations derived from models pre-trained on large-scale datasets have shown their generalizability on a variety of audio analysis tasks. Despite this generalizabilit…
Modeling Beats and Downbeats with a Time-Frequency Transformer
Yun-Ning Hung, Ju-Chiang Wang, Xuchen Song +2
Transformer is a successful deep neural network (DNN) architecture that has shown its versatility not only in natural language processing but also in music information retrieval (M…
To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions
Ju-Chiang Wang, Yun-Ning Hung, Jordan B. L. Smith
Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e.g., 'A', 'B', and 'C'). However, explicitly identifyin…
Feature-informed Latent Space Regularization for Music Source Separation
Yun-Ning Hung, Alexander Lerch
The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning…