A singer in a kitchen gives twelve verses of a ballad, each to the same tune and none of them quite alike. The researcher who recorded her will later sit with headphones and a pencil, replaying two seconds at a time, and will have to decide for every note what pitch it was, how long it lasted, and whether the difference between verse three and verse seven is a variant worth writing down or the ordinary give of a living performance. The labour is considerable, and its results depend heavily on who is listening.

Computational tools can now take over part of the measuring. Pitch trackers return the fundamental frequency of a monophonic line frame by frame; onset detectors propose note boundaries; alignment algorithms find which moment in one recording corresponds to which moment in another. Surveys of automatic transcription are frank that fully automatic notation of real recordings is still unreliable, particularly outside the piano and outside equal temperament (Benetos et al., 2019). The opportunity for field researchers lies elsewhere: in semi-automatic work, where software measures and aligns and the researcher interprets.

This article proposes a workflow of that kind for solo or lightly accompanied oral repertoire, such as unaccompanied song, fiddle or flute tunes, and chant. It is written for researchers who know their repertoire well and their software less well. The workflow is put forward as a design. It has not been validated on a field collection in this article, and a later section states what validation would involve.

Two discussions frame the design: the long argument within ethnomusicology about what a transcription is for, and the technical literature on pitch tracking and alignment.

The workflow has eight steps, shown in sequence in Figure 1. Table 1 lists the tools suggested for each and the file that each step should leave behind. The principle throughout is that the software proposes and the researcher decides, and that each decision is saved in a form another person can open.

Field recordings are seldom clean. Where several people sing together, where the melody instrument plays over a drone or a guitar, or where the room is loud, pitch tracking of the kind described degrades quickly, and the researcher should expect to do step four largely by hand or to limit the workflow to the solo items in a collection. Heterophonic and polyphonic textures are beyond it. The steps are separable, however, and segmentation, tonal reference and deposit remain useful when tracking is not.

Questions of ownership and control run through every step. Anthony Seeger has shown how poorly copyright law fits music held in common or by lineage (Seeger, 1992), and work on the ethics of music information retrieval points out that turning a community's songs into data creates new ways for them to be reused without consent (Holzapfel et al., 2018). The workflow therefore places consent at the first step and review at the last, and treats the decision about what is deposited openly as belonging to the performers. Tools serve fieldwork only for as long as the people recorded remain partners in it.

A practical point concerns where the work is done. Every tool named here runs offline on an ordinary laptop, so segmentation and a first pitch track can be made in the evening after a session while the performer is still nearby to answer questions. Doing so changes the next day's recording, because gaps and doubtful passages are noticed in time to ask for a repeat. It also means that files multiply quickly. A fixed naming scheme that carries the item identifier, the step number and a version through every annotation, pitch track and score file is dull to maintain and is the only thing that keeps a collection of several hundred items usable a year later.

The workflow is a proposal and has not been applied or validated here. Three things would need to be shown. The first is agreement: whether transcriptions produced this way by different researchers from the same recordings differ less than transcriptions made by ear alone. The second is effort, since correcting a poor pitch track can take longer than transcribing from scratch, and the balance will vary with recording quality. The third is acceptance, meaning whether performers recognise their own music in the result and whether the variants the alignment reports are differences they consider meaningful.

There are also limits that no test will remove. Dynamic time warping assumes that two renditions contain the same events in the same order; it copes badly when a singer inserts or omits a phrase, and such cases must be segmented first. Pitch histograms presuppose a stable tonal reference, which unaccompanied singers do not always keep across a long song. And a time-aligned score is still a reduction. It records pitch and timing and leaves out timbre, gesture, words unspoken and the occasion, which fieldnotes and the recording itself must carry.

Pitch tracking and audio alignment will not transcribe a field collection, but they can measure what the ear estimates and keep every written note attached to its sound. The eight-step workflow proposed here arranges freely available tools so that each supports a decision the researcher must still make and leaves a file that others can inspect. Its usefulness for a given repertoire is an open question that field researchers are best placed to answer, ideally by trying it on a small set of recordings alongside their usual practice and reporting where it helped and where it did not.

To be declared by the author(s) before publication.

To be declared by the author(s) before publication.