## 1 Introduction
Most symbolic voice separation approaches are based on voice leading rules, which have been investigated and described from a cognitive perspective in a few different works [26; 27; 28]. Among these rules, three main principles emerge: (1) large melodic intervals between consecutive notes in a single voice should be avoided; (2) two voices should not, in general, cross in pitch; and (3) the stream of notes within a single voice should be relatively continuous, without long gaps of silence, ensuring temporal continuity. There are many different definitions of what precisely constitutes a voice, both perceptually and musically, discussed more fully in [25]; however, for our purposes, a voice is quite simply defined as the notes sung by a single vocalist. Therefore, our interest in voice separation models lies with those that separate notes into strictly monophonic voices (i.e., those that do not allow for concurrent notes), rather than polyphonic voices as in [29]. We would also like our chosen model to be designed to be run in a mostly unsupervised fashion, rather than being designed for use with human interaction (as in [30]), and for it not to require background information about the piece, such as time signature or metrical information (as in [31]). While many voice separation models remain that meet our criteria [32; 33; 34; 35; 36], the one described in [37] is the most promising for our use because it both (1) achieves state-of-the-art performance and (2) can be applied directly to live performance. In this work, we present a system able to perform multi-pitch detection of polyphonic a cappella vocal music, as well as assign each detected pitch to a particular voice (soprano, alto, tenor or bass), where the number of voices is known a priori. Our approach uses an acoustic model for multi-pitch detection based on probabilistic latent component analysis (PLCA), which is modified from the model proposed in [21], and an HMM-based music language model for voice assignment based on the model of [37]. 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 [PERSON] chorales and barbershop quartets. The proposed system is evaluated both in terms of multi-pitch detection and voice assignment, where it reaches an F-measure of over 70% and 50% for the two respective tasks. The remainder of this paper is organised as follows. In Section 2, we describe the proposed approach, consisting of the acoustic model, the music language model and model integration. In Section 3, we report on experimental results using two datasets comprising recordings of vocal quartets. Section 4 closes with conclusions and perspectives for future work.