Multi-Instrument Automatic Music Transcription With Self-Attention-Based Instance Segmentation
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING(2020)
摘要
Multi-instrument automatic music transcription (AMT) is a critical but less investigated problem in the field of music information retrieval (MIR). With all the difficulties faced by traditional AMT research, multi-instrument AMT needs further investigation on high-level music semantic modeling, efficient training methods for multiple attributes, and a clear problem scenario for system performance evaluation. In this article, we propose a multi-instrument AMT method, with signal processing techniques specifying pitch saliency, novel deep learning techniques, and concepts partly inspired by multi-object recognition, instance segmentation, and image-to-image translation in computer vision. The proposed method is flexible for all the sub-tasks in multi-instrument AMT, including multi-instrument note tracking, a task that has rarely been investigated before. State-of-the-art performance is also reported in the sub-task of multi-pitch streaming.
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关键词
Instruments, Task analysis, Music, Multiple signal classification, Hidden Markov models, Speech processing, Deep learning, Automatic music transcription, deep learning, multi-pitch estimation, multi-pitch streaming, self-attention
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