Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models

arXiv:2404.06818v1 Announce Type: cross
Abstract: In recent years, advancements in neural network designs and the availability of large-scale labeled datasets have led to significant improvements in the accuracy of piano transcription models. However, most previous work focused on high-performance offline transcription, neglecting deliberate consideration of model size. The goal of this work is to implement real-time inference for piano transcription while ensuring both high performance and lightweight. To this end, we propose novel architectures for convolutional recurrent neural networks, redesigning …

abstract accuracy arxiv autoregressive autoregressive models availability cs.lg cs.sd datasets designs eess.as however improvements network neural network offline performance real-time scale transcription type work