Automatic Music Transcription using efficient Multi-Source SI-PLCA (+GPU support)

Overview

This code performs automatic music transcription using an efficient version of the Multi-Source Shift-Invariant Probabilistic Latent Component Analysis model. Optional GPU processing considerably improves speed (works faster than real-time). The output is a non-binary piano-roll representation which if post-processed (e.g. thresholded) can be used to derive a MIDI-like representation of the transcription. The present demo includes 3 sets of piano templates computed from isolated note samples from the MAPS database.

Related publications

E. Benetos, S. Cherla, and T. Weyde, “An effcient shift-invariant model for polyphonic music transcription,” in 6th International Workshop on Machine Learning and Music, Prague, Czech Republic, 2013.: [More Details] [BIBT_EX] [URL (ext.)]; @inproceedings{emmanouil2013a, author = {Emmanouil Benetos and Srikanth Cherla and Tillman Weyde}, title = {An effcient shift-invariant model for polyphonic music transcription}, address = {Prague, Czech Republic}, booktitle = {6th International Workshop on Machine Learning and Music}, month = {September}, year = {2013} }
E. Benetos and T. Weyde, Multiple-F0 estimation and note tracking for MIREX 2013 using an efficient latent variable model. 2013.: [More Details] [BIBT_EX] [URL (ext.)]; @proceedings{emmanouil2013a, author = {Emmanouil Benetos and Tillman Weyde}, title = {Multiple-F0 estimation and note tracking for MIREX 2013 using an efficient latent variable model}, booktitle = {Music Information Retrieval Evaluation eXchange}, month = {November}, year = {2013} }

Members

Manager: Emmanouil Benetos, Srikanth Cherla, Tillman Weyde
Developer: Emmanouil Benetos, Srikanth Cherla