Audio-to-score singing transcription based on a CRNN-HSMM hybrid model

Abstract

This paper describes an automatic singing transcription (AST) method that estimates a human-readable musical score of a sung melody from an input music signal. Because of the considerable pitch and temporal variation of a singing voice, a naive cascading approach that estimates an F0 contour and quantizes it with estimated tatum times cannot avoid many pitch and rhythm errors. To solve this problem, we formulate a unified generative model of a music signal that consists of a semi-Markov language model representing the generative process of latent musical notes conditioned on musical keys and an acoustic model based on a convolutional recurrent neural network (CRNN) representing the generative process of an observed music signal from the notes. The resulting CRNN-HSMM hybrid model enables us to estimate the most-likely musical notes from a music signal with the Viterbi algorithm, while leveraging both the grammatical knowledge about musical notes and the expressive power of the CRNN. The experimental results showed that the proposed method outperformed the conventional state-of-the-art method and the integration of the musical language model with the acoustic model has a positive effect on the AST performance.

MusicYOLO: A Vision-Based Framework for Automatic Singing Transcription

“…In [36], the author proposed a Bayesian hierarchical hidden semi-Markov model (HHSMM), which generates a note sequence and consists of three sub-models describing local keys, pitches, and onset score times. Later, a CRNN-HSMM hybrid model was proposed in [37], estimating the most likely notes from the music signal using the Viterbi algorithm. This method improved the performance of AST and was superior to HSMM-based method, the most advanced method at that time [36].…”

A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation

“…Multiple methods have been proposed for estimating notes from pitch posteriorgrams e.g. using median filtering [11], Hidden Markov Models [16] or neural networks [20,21]. While most approaches consider each semitone independently, some approaches attempt to model the interactions between notes, using spectral likelihood models [1,18], or music language models [3,17].…”