A Hybrid Recurrent Neural Network For Music Transcription

2014/11/06 by Siddharth Sigtia, Sigtia, Siddharth, Emmanouil Benetos +9
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Speech and Audio Processing

paper · doi:10.48550/arxiv.1411.1623

openalex publication_date 2014/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance. We use recurrent neural networks (RNNs) and their variants as music language models (MLMs) and present a generative architecture for combining these models with predictions from a frame level acoustic classifier. We also compare different neural network architectures for acoustic modeling. The proposed model computes a distribution over possible output sequences given the acoustic input signal and we present an algorithm for performing a global search for good candidate transcriptions. The performance of the proposed model is evaluated on piano music from the MAPS dataset and we observe that the proposed model consistently outperforms existing transcription methods.

Citations

• Sequence Transduction with Recurrent Neural Networks
• Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

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