27 citations · 70 across the 22 of their papers we have counts for
22 papers
EG-VAE: A Unified Framework for Electric Guitar Tone Transfer and Removal
Yen-Tung Yeh, Yun-Ning, Hung +1
Electric guitar tone transfer (EGTT) and tone removal (EGTR) are two fundamental tasks in guitar tone modeling: EGTT replaces a recording's tone with that of a reference, while EGT…
Rethinking Automatic Music Mixing as Sequential Stem Blending
Yen-Tung Yeh, Chung-Jui Chan, Yun-Ning +2
Automatic music mixing, the task of automatically combining individual audio tracks into a cohesive mixture, is typically addressed by parallelized architectures that process all i…
A Foundation Model for Music Informatics
Minz Won, Yun-Ning Hung, Duc Le
This paper investigates foundation models tailored for music informatics, a domain currently challenged by the scarcity of labeled data and generalization issues. To this end, we c…
Scaling Up Music Information Retrieval Training with Semi-Supervised Learning
Yun-Ning Hung, Ju-Chiang Wang, Minz Won +1
In the era of data-driven Music Information Retrieval (MIR), the scarcity of labeled data has been one of the major concerns to the success of an MIR task. In this work, we leverag…
Music Source Separation with Band-Split RoPE Transformer
Wei-Tsung Lu, Ju-Chiang Wang, Qiuqiang Kong +1
Music source separation (MSS) aims to separate a music recording into multiple musically distinct stems, such as vocals, bass, drums, and more. Recently, deep learning approaches s…
Multitrack Music Transcription with a Time-Frequency Perceiver
Wei-Tsung Lu, Ju-Chiang Wang, Yun-Ning Hung
Multitrack music transcription aims to transcribe a music audio input into the musical notes of multiple instruments simultaneously. It is a very challenging task that typically re…
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…
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…
AVASpeech-SMAD: A Strongly Labelled Speech and Music Activity Detection Dataset with Label Co-Occurrence
Yun-Ning Hung, Karn N. Watcharasupat, Chih-Wei Wu +4
We propose a dataset, AVASpeech-SMAD, to assist speech and music activity detection research. With frame-level music labels, the proposed dataset extends the existing AVASpeech dat…
Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision
Yun-Ning Hung, Gordon Wichern, Jonathan Le Roux
Most music source separation systems require large collections of isolated sources for training, which can be difficult to obtain. In this work, we use musical scores, which are co…
Multitask learning for instrument activation aware music source separation
Yun-Ning Hung, Alexander Lerch
Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years. Nevertheless, most of the existing systems focus excl…
Score-informed Networks for Music Performance Assessment
Jiawen Huang, Yun-Ning Hung, Ashis Pati +2
The assessment of music performances in most cases takes into account the underlying musical score being performed. While there have been several automatic approaches for objective…
Musical Composition Style Transfer via Disentangled Timbre Representations
Yun-Ning Hung, I-Tung Chiang, Yi-An Chen +1
Music creation involves not only composing the different parts (e.g., melody, chords) of a musical work but also arranging/selecting the instruments to play the different parts. Wh…
Learning Disentangled Representations for Timber and Pitch in Music Audio
Yun-Ning Hung, Yi-An Chen, Yi-Hsuan Yang
Timbre and pitch are the two main perceptual properties of musical sounds. Depending on the target applications, we sometimes prefer to focus on one of them, while reducing the eff…
Multitask learning for frame-level instrument recognition
Yun-Ning Hung, Yi-An Chen, Yi-Hsuan Yang
For many music analysis problems, we need to know the presence of instruments for each time frame in a multi-instrument musical piece. However, such a frame-level instrument recogn…
Frame-level Instrument Recognition by Timbre and Pitch
Yun-Ning Hung, Yi-Hsuan Yang
Instrument recognition is a fundamental task in music information retrieval, yet little has been done to predict the presence of instruments in multi-instrument music for each time…
Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss
Lang-Chi Yu, Yi-Hsuan Yang, Yun-Ning Hung +1
A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most…