## 1 Introduction
While the aforementioned approaches focus on the task of polyphonic piano transcription due to the presence of sufficiently large piano-specific datasets, the recently released MusicNet dataset [PERSON] et al. (2015) provides a large corpus for multi-instrument music suitable for training deep learning methods for the task of polyphonic music transcription. Convolutional neural networks were also used in [PERSON] et al. (2015) for learning salience representations for fundamental frequency estimation in polyphonic audio recordings. Multi-pitch detection of vocal music represents a significant step up in difficulty as the variety of sounds produced by a single singer can be both unique and wide-ranging. 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, [PERSON] and [PERSON] [PERSON] and [PERSON] (2015) 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). In [PERSON] et al. (2015), an acoustic model based on spectrogram factorisation was proposed for multi-pitch detection of such vocal quartets. A small group of methods has attempted to go beyond multi-pitch detection, towards instrument assignment (also called timbre tracking) [PERSON] et al. (2015); [PERSON] et al. (2015); [PERSON] et al. (2015), where systems detect multiple pitches and assign each pitch to a specific source that produced it. [PERSON] et al. [PERSON] et al. (2015) tracked individual instruments in polyphonic instrumental music using a spectrogram factorisation approach with continuity constraints controlled by a hidden Markov model (HMM). To the authors' knowledge, no methods have yet been proposed to perform both multi-pitch detection and instrument/voice assignment on polyphonic vocal music. An emerging area of automatic music transcription attempts to combine acoustic models (those based on audio information only) with music language models, which model sequences of notes and other music cues based on knowledge from music theory or from constraints automatically derived from symbolic music data. This is in direct analogy to automatic speech recognition systems, which typically combine an acoustic model with a spoken language model. [PERSON] and [PERSON] [PERSON] and [PERSON] (2015), for example, combined acoustic and music language models for polyphonic music transcription,where the musicological model estimates the probability of a detected note sequence. Another example of such an integrated system is the work by [PERSON] et al. [16], which combined neural network-based acoustic and music language models for multi-pitch detection in piano music. The system used various types of neural networks for the acoustic component (feedforward, recurrent, convolutional) along with a recurrent neural network acting as a language model for modelling the correlations between pitch combinations over time. Combining instrument assignment with this idea of using a music language model, it is natural to look towards the field of voice separation [25], which involves the separation of pitches into streams of notes, called voices, and is mainly addressed in the context of symbolic music processing. It is important to note that voice separation, while similar to our task of voice assignment, is indeed a distinct task. Specifically, while both involve an initial step of separating the incoming notes into voices, voice assignment involves a further step of labelling each of those voices as a specific part or instrument, in our case soprano, alto, tenor or bass.