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http://mac.citi.sinica.edu.tw/~yang/
yhyang@ailabs.tw
Yi-Hsuan Yang Ph.D. 1,2
1 Taiwan AI Labs
2 Research Center for IT Innovation, Academia Sinica
Music AI Research (Common Approach)
? Algorithmic composition
?MIDI in, MIDI out
? Limitations
?Lack expressivity: Cannot be directly listened to
?Some music genres are not ¡°written language¡±
2
NLU
NLG
(Music encoding used by openAI¡¯s MuseNet model)
Music AI Research (at the Taiwan AILabs)
3
? audio in, audio out
? audio ¡ú audio: source separation (SS) [denoising]
? audio ¡ú score: music transcription (MT) [ASR]
? score ¡ú score: composition [NLG]
? score ¡ú audio: synthesis [TTS]
The ¡°MIR4generation¡± Pipeline
4
? The transcription model predicts the pitch, onset/offset
timing (in absolute time), and velocity (dynamics)
? and the beat/downbeat model provides the underlying metrical grid
(in symbolic time) of the music
? So that the composition model can also learn to generate
expressive music
Demo
5
? We focus on piano music currently
https://soundcloud.com/yating_ai/sets/ismir-2019-
submission/
Piano Separation
? https://ailabs.tw/human-
interaction/transcription4generation/
6
Music Transcription
? https://ailabs.tw/human-
interaction/transcription4generation/
7
8
Music Composition: ¡°Yating Live Music¡±
Jazz Piano Trio Generation: JazzRNN
? Given an 8-bar chord sequence, generate an 8-bar piano
performance with an RNN model
? Use a Transformer-like model to extend it
? Use another RNN model to improvise the bass part, given the
AI-generated piano and a random human-made drum loop
9
? https://ailabs.tw/hu
man-interaction/ai-
jazz-bass-player/
? https://www.youtube.com/watch?v=9ZIJrr6lmHg
Jamming with Yating
10
? Yeh et al., ¡°Learning to generate Jazz and Pop piano music from audio via MIR
techniques,¡± ISMIR-LBD 2019
? Hsiao et al., ¡°Jamming with Yating: Interactive demonstration of a music
composition AI,¡± ISMIR-LBD 2019

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