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This paper presents a multi-pitch detection and voice assignment method applied to audio recordings containing a cappella performances with multiple singers. A novel approach combining an acoustic model for multi-pitch detection and a music language model for voice separation and assignment is proposed. The acoustic model is a spectrogram factorization process based on Probabilistic Latent Component Analysis (PLCA), driven by a 6-dimensional dictionary with pre-learned spectral templates. The voice separation component is based on hidden Markov models that use musicological assumptions. By integrating the models, the system can detect multiple concurrent pitches in vocal music and assign each detected pitch to a specific voice corresponding to a voice type such as soprano, alto, tenor or bass (SATB). This work focuses on four-part compositions, and evaluations on recordings of Bach Chorales and Barbershop quartets show that our integrated approach achieves an F-measure of over 70% for frame-based multipitch detection and over 45% for four-voice assignment.
|Title of host publication||Proceedings of the 18th International Society for Music Information Retrieval Conference|
|Place of Publication||Suzhou, China|
|Number of pages||8|
|Publication status||Published - 27 Oct 2017|
|Event||18th International Society for Music Information Retrieval Conference - National University of Singapore Research Institute (NUSRI), Suzhou, China|
Duration: 23 Oct 2017 → 27 Oct 2017
|Conference||18th International Society for Music Information Retrieval Conference|
|Abbreviated title||ISMIR 2017|
|Period||23/10/17 → 27/10/17|
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