12 citations · 17 across the 10 of their papers we have counts for
11 papers
Harmonica: Accurate and Lightweight Instrument-Agnostic Music Transcription
Longshen Ou, Héctor Martel, Joe Hennessy-Priest +1
This paper introduces Harmonica, a family of instrument-agnostic music transcription models built around multi-depth harmonic convolution. At each model scale, Harmonica achieves t…
PhraseVAE and PhraseLDM: Latent Diffusion for Full-Song Multitrack Symbolic Music Generation
Longshen Ou, Ye Wang
This technical report presents a new paradigm for full-song symbolic music generation. Existing symbolic models operate on note-attribute tokens and suffer from extremely long sequ…
Lead Instrument Detection from Multitrack Music
Longshen Ou, Yu Takahashi, Ye Wang
Prior approaches to lead instrument detection primarily analyze mixture audio, limited to coarse classifications and lacking generalization ability. This paper presents a novel app…
Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization
Longshen Ou, Jingwei Zhao, Ziyu Wang +3
We present a unified framework for automatic multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, including…
Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation
Longshen Ou, Xichu Ma, Ye Wang
Despite progress in melody-to-lyric generation, a substantial singability gap remains between machine-generated lyrics and those written by human lyricists. In this work, we aim to…
Songs Across Borders: Singable and Controllable Neural Lyric Translation
Longshen Ou, Xichu Ma, Min-Yen Kan +1
The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the ou…
Deep Audio-Visual Singing Voice Transcription based on Self-Supervised Learning Models
Xiangming Gu, Wei Zeng, Jianan Zhang +2
Singing voice transcription converts recorded singing audio to musical notation. Sound contamination (such as accompaniment) and lack of annotated data make singing voice transcrip…
Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription
Longshen Ou, Xiangming Gu, Ye Wang
Automatic speech recognition (ASR) has progressed significantly in recent years due to the emergence of large-scale datasets and the self-supervised learning (SSL) paradigm. Howeve…
MM-ALT: A Multimodal Automatic Lyric Transcription System
Xiangming Gu, Longshen Ou, Danielle Ong +1
Automatic lyric transcription (ALT) is a nascent field of study attracting increasing interest from both the speech and music information retrieval communities, given its significa…
Exploring Transformer's potential on automatic piano transcription
Longshen Ou, Ziyi Guo, Emmanouil Benetos +2
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic…
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
Bozhou Chen, Kaixin Zhang, Longshen Ou +3
Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-paramete…