Development of Large Annotated Music Datasets using HMM-based Forced Viterbi Alignment

TL;DR

This paper introduces a method using HMM-based forced Viterbi alignment with predefined guitar exercises to efficiently generate annotated music datasets, facilitating automatic music transcription research.

Contribution

It presents a streamlined approach for creating instrument-specific datasets with precise transcriptions, especially for monophonic instruments like guitar.

Findings

Generated an acoustic guitar dataset with accurate time-aligned transcriptions.

Achieved average transcription accuracy within 5ms of manual verification.

Demonstrated the method's potential for efficient dataset creation for AMT systems.

Abstract

Datasets are essential for any machine learning task. Automatic Music Transcription (AMT) is one such task, where considerable amount of data is required depending on the way the solution is achieved. Considering the fact that a music dataset, complete with audio and its time-aligned transcriptions would require the effort of people with musical experience, it could be stated that the task becomes even more challenging. Musical experience is required in playing the musical instrument(s), and in annotating and verifying the transcriptions. We propose a method that would help in streamlining this process, making the task of obtaining a dataset from a particular instrument easy and efficient. We use predefined guitar exercises and hidden Markov model(HMM) based forced viterbi alignment to accomplish this. The guitar exercises are designed to be simple. Since the note sequence are already defined, HMM based forced viterbi alignment provides time-aligned transcriptions of these audio files. The onsets of the transcriptions are manually verified and the labels are accurate up to 10ms, averaging at 5ms. The contributions of the proposed work is two fold, i) a well streamlined and efficient method for generating datasets for any instrument, especially monophonic and, ii) an acoustic plectrum guitar dataset containing wave files and transcriptions in the form of label files. This method will aid as a preliminary step towards building concrete datasets for building AMT systems for different instruments.