Transcription of Polyphonic Vocal Music with a Repetitive Melodic Structure

Abstract

This paper presents a novel method for transcription of folk music that exploits its specifics to improve transcription accuracy. In contrast to most commercial music, folk music recordings may contain various inaccuracies as they are usually performed by amateur musicians and recorded in the field. If we use standard approaches for transcription, these inaccuracies are reflected in erroneous pitch estimates. On the other hand, the structure of western folk music is usually simple as songs are often composed of repeated melodic parts. In our approach we make use of these repetitions to increase transcription robustness and improve its accuracy. The proposed method fuses three sources of information: (1) frame-based multiple F0 estimates, (2) song structure, and (3) pitch drift estimates. It first selects a representative segment of the analyzed song and aligns all the other segments to it considering temporal as well as frequency deviations. Information from all segments is summarized and used in a two-layer probabilistic model based on explicit duration HMMs, to segment frame-based information into notes. The method is evaluated with state-of-the-art transcription methods where we show that significant improvement in accuracy can be achieved.

Automatic Transcription of Polyphonic Vocal Music

“…The timbre of two singers' voices can differ greatly, and even for a single singer, different vowel sounds produce extremely varied overtone patterns. For vocal music, Bohak and Marolt [20] propose a method for transcribing folk music containing both instruments and vocals, which takes advantage of melodic repetitions present in that type of music using a musicological model for note-based transcription. A less explored type of music is a cappella; in particular, vocal quartets constitute a traditional form of Western music, typically dividing a piece into multiple vocal parts such as soprano, alto, tenor and bass (SATB).…”

“…Compared to our previous work [38], this model contains a new dynamic dictionary voice type assignment step (described in Section 2.3), which accounts for its increased performance. Although previous work has integrated musicological information for note event modelling [16,20,24], to the authors' knowledge, this is the first attempt to incorporate an acoustic model with a music language model for the task of voice or instrument assignment from audio, as well as the first attempt to propose a system for voice assignment in polyphonic a cappella music. The approach described in this paper focuses on recordings of singing performances by vocal quartets without instrumental accompaniment; to that end, we use two datasets containing a capella recordings of Bach chorales and barbershop quartets.…”