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• Talks
• Automatic Music Composition with Transformers, in Jan 2021
• Machine Learning for Creative AI Applications in Music @ The 5th Taiwanese Music and Audio Computing Workshop (link)
• Research at MAC Lab, Academia Sincia, in 2017
• Music information retrieval @ Annual Conference of the Taiwan Data Science Foundation (台灣資料科學愛好者年會) 2016
• Datasets
• EMOPIA: a multimodal dataset comprising audio+MIDI of emotion-annotated pop piano solo pieces
• DadaGP: a dataset of ~26k GuitarPro songs in ~800 genres, converted to a token sequence format for generative language models like GPT2, TransformerXL, etc
• CCMED & WWMED: corpora of Western classical music excerpts (WCMED) and Chinese classical music excerpts (CCMED) annotated with emotional valence and arousal values (ICASSP’20 paper-a)
• #nowplaying-RS: a new benchmark dataset for building context-aware music recommender systems (SMC’18 paper)
• Symbolic-Musical-Datasets: list of symbolic musical datasets, including lead sheets and MIDIs
• Lakh Pianoroll Dataset (LPD): a collection of 174,154 unique multi-track piano-rolls derived from the MIDI files in Lakh MIDI Dataset (LMD), used in our MuseGAN paper (AAAI’18 paper)
• iKala: 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX SVS 2014-2016) (ICASSP’15 paper)
• Su Dataset for automatic music transcription in piano solo, piano quintet, string quartet, violin sonata, choir, and symphony (ISMIR’16 and ISMIR’15 papers)
• MACLab Dataset for violin offset detection (ISMIR’15 paper)
• MACLab Dataset for guitar playing techniques (ISMIR’15 and ISMIR’14 papers)
• SCREAM-MAC-EMT Dataset for expression analysis in violin (ISMIR’15 paper)
• Octave dual-tone dataset (SMC’14 paper)
• The AMG1608 dataset for personalized music emotion recognition (ICASSP’15 paper)
• The CH818 dataset for music emotion recognition in Chinese Pop songs
• The DEAM and MediaEval dataset for dynamic and static music emotion recognition (used in the ‘Emotion in Music’ Task in MediaEval 2013-2015)
• CAL500exp Dataset for time-varying music auto-tagging (ICME’14 paper)
• CAL10k: 10k songs with 140 genre tags (TMM’13 paper)
• LiveJournal: 40k blog articles with user mood labels and music tags (TMM’13 paper)

• Codes
• MuseMorphose: a Transformer-VAE architecture for per-bar music style transfer
• Variable-length piano infilling: a XLNet-based model for inpainting a piano sequence with variable number of notes (up to 128 notes) (ISMIR’21 paper)
• LoopTest: a benchmark of audio-domain musical phrase generation using drum loops (ISMIR’21 paper)
• drum-aware4beat: drum-aware ensemble architecture for improved joint musical beat and downbeat tracking (SPL’21 paper)
• CP Transformer: the world’s first neural sequence model for music generation at full-song length (AAAI’21 paper)
• Pop Music Transformer: a neural sequence model for beat-based automatic piano music composition (MM’20 paper)
• MIDI toolkit: Designed for handling MIDI in symbolic timing (ticks), which is the native format of MIDI timing; we keep the midi parser as simple as possible, and offer several useful utility functions
• Singer-identification-in-artist20: the convolutional recurrent neural network with melody model for singer identification with the shuffle-and-remix data augmentation technique (ICASSP’20 paper-c)
• Speech-to-Singing Conversion: an end to end model for converting speech voice into singing voice (ICASSP’20 paper-b)
• Latent inspector for LeadsheetVAE model
• DrumVAE: a recurrent VAE model for generating regular drum patterns (MILC’19 paper)
• musical-ml-web-demo-minimal-template (MILC’19 paper)
• DANtest: a simple framework based on discriminative adversarial networks for testing different adversarial losses (arxiv paper)
• Learning to match transient sound events using attentional similarity for few-shot sound recognition (ICASSP’19 paper-c)
• Audio-to-midi: faster version of melodic-segnet
• melodic-segnet: for vocal melody and general melody extraction (ICASSP’19 paper-b)
• Hung’s instrument streaming model (ICASSP’19 paper-a)
• Hypergraph embedding: implementation of a graph embedding learning method for hypergraphs (CIKM’18 paper)
• LeadsheetVAE: a recurrent VAE model for generating lead sheets (ISMIR-LBD’18 paper-b)
• Lead sheet generation and arrangement: a generative adversarial network for generating lead sheets and their arrangement (ICMLA’18 paper)
• Pypianoroll: an ppen source Python package for handling multitrack pianorolls (ISMIR-LBD’18 paper)
• BMuseGAN: an extended version of MuseGAN that uses binary neurons (ISMIR’18 paper)
• Hung’s instrument recognizer: a CNN based model that performs frame-level instrument prediction (i.e. instrument activity detection) (ISMIR’18 paper)
• M&mnet: a model that uses attentional supervision to deal with transient sound event detection (IJCAI’18 paper)
• pop-music-highlighter: a convolutional attention network for music highlight detection (i.e. thumbnailing), based on emotion labels (arxiv’18 paper)
• SEN: the similarity embedding network we proposed to deal with music medley and other music puzzle games (AAAI’18 paper)
• MuseGAN: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment (AAAI’18 paper)
• MidiNet: a convolutional generative adversarial network for symbolic-domain music (melody) generation (ISMIR’17 paper) [the PyTorch version]
• The clip2frame CNN algorithm for event localization in music auto-tagging (MM’16 paper)
• Adaptive linear mapping model (ALMM) for conten-based next item recommendation (RecSys’16 paper)
• Informed group-sparse representation for singing voice separation (SPL’17 paper)
• Polar n-complex and n-bicomplex singular value decomposition and principal component pursuit (TSP’16 paper)
• The SPORCO library for convolutional sparse coding algorithm, developed by Brendt Wohlberg (TASLP’16 paper)
• Complex and quaternionic principal component pursuit for source separation (SPL’15 paper)
• Musical onset detection using constrained linear reconstruction (SPL’15 paper)
• The Acoustic Emotion Gaussians (AEG) Model for music emotion recognition of valence and arousal values (TAC’15 and MM’12 papers)
• AWtoolbox for characterizing audio information using sparse coding based audio words (MM’14 paper)
• Multiple low rank representation (MLRR) for source separation (ISMIR’13 paper) (related report by Alex Berrian in 2014)