New journal papers from the Machine Listening Lab
The Machine Listening Lab has recently had great success towards publishing journal papers related to the lab’s research priorities on developing new machine learning and signal processing methodologies for audio and timeseries analysis. Our new work ranges from new methods for speaker anti-spoofing, to visibility graphs for large-scale time series analysis, and on new evaluation methodologies for music prediction and transcription.
The list of our recently accepted and published journal papers can be found below; many of them are freely available or have links to preprints so you can read them already:
- Bhusan Chettri, Tomi Kinnunen, and Emmanouil Benetos, “Deep generative variational autoencoding for replay spoof detection in automatic speaker verification“, Computer Speech and Language, vol. 63, article no. 101092, 2020.
Free access paper (available until 26 May 2020)
- Delia Fano Yela, Florian Thalmann, Vincenzo Nicosia, Dan Stowell, and Mark Sandler, “Online visibility graphs: Encoding visibility in a binary search tree“, Physical Review Research, vol. 2, no. 2, article no. 023069, 2020.
- Adrien Ycart and Emmanouil Benetos, “Learning and evaluation methodologies for polyphonic music sequence prediction with LSTMs“, IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 28, pp. 1328-1341, 2020.
- Adrien Ycart, Lele Liu, Emmanouil Benetos, and Marcus T. Pearce, “Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription“, Transactions of the International Society for Music Information Retrieval, vol. 3, no. 1, pp. 68-81, June 2020.