Classification Of Spontaneous And Scripted Speech For Multilingual Audio
Shahar Elisha, Andrew McDowell, Mariano Beguerisse-Díaz, Emmanouil Benetos
We introduce DrummerNet, a drum transcription system that is trained in an unsupervised manner. DrummerNet does not require any ground-truth transcription and, with the data-scalability of deep neural networks, learns from a large unlabeled dataset. In DrummerNet, the target drum signal is first passed to a (trainable) transcriber, then reconstructed in a (fixed) synthesizer according to the transcription estimate. By training the system to minimize the distance between the input and the output audio signals, the transcriber learns to transcribe without ground truth transcription. Our experiment shows that DrummerNet performs favorably compared to many other recent drum transcription systems, both supervised and unsupervised.
Shahar Elisha, Andrew McDowell, Mariano Beguerisse-Díaz, Emmanouil Benetos
Wojciech Reise, Ximena Fernández, Maria Dominguez, Heather A. Harrington, Mariano Beguerisse-Díaz
A. Ghazimatin, E. Garmash, G. Penha, K. Sheets, M. Achenbach, O. Semerci, R. Galvez, M. Tannenberg, S. Mantravadi, D. Narayanan, O. Kalaydzhyan, D. Cole, B. Carterette, A. Clifton, P. N. Bennett, C. Hauff, M. Lalmas-Roelleke